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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.feature as cfeature\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "## EOF 关键包\n",
    "from eofs.standard import Eof\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  ###防止无法显示中文并设置黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False  ###用来正常显示负号"
   ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 89, lon: 180, time: 2016, nbnds: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float32 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon        (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0\n",
       "  * time       (time) datetime64[ns] 1854-01-01 1854-02-01 ... 2021-12-01\n",
       "Dimensions without coordinates: nbnds\n",
       "Data variables:\n",
       "    time_bnds  (time, nbnds) float64 9.969e+36 9.969e+36 ... 9.969e+36 9.969e+36\n",
       "    sst        (time, lat, lon) float32 ...\n",
       "Attributes: (12/37)\n",
       "    climatology:               Climatology is based on 1971-2000 SST, Xue, Y....\n",
       "    description:               In situ data: ICOADS2.5 before 2007 and NCEP i...\n",
       "    keywords_vocabulary:       NASA Global Change Master Directory (GCMD) Sci...\n",
       "    keywords:                  Earth Science &gt; Oceans &gt; Ocean Temperature &gt; S...\n",
       "    instrument:                Conventional thermometers\n",
       "    source_comment:            SSTs were observed by conventional thermometer...\n",
       "    ...                        ...\n",
       "    creator_url_original:      https://www.ncei.noaa.gov\n",
       "    license:                   No constraints on data access or use\n",
       "    comment:                   SSTs were observed by conventional thermometer...\n",
       "    summary:                   ERSST.v5 is developed based on v4 after revisi...\n",
       "    dataset_title:             NOAA Extended Reconstructed SST V5\n",
       "    data_modified:             2022-01-07</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-089def1a-d8dd-4333-b4fd-8d706c1bbea8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-089def1a-d8dd-4333-b4fd-8d706c1bbea8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 2016</li><li><span>nbnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-890de092-b5b8-4c99-a0a5-c77017170b20' class='xr-section-summary-in' type='checkbox'  checked><label for='section-890de092-b5b8-4c99-a0a5-c77017170b20' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-059bacb5-f897-4d16-889e-e48a06f5cecc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-059bacb5-f897-4d16-889e-e48a06f5cecc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abad8101-f58c-4de4-8335-4f583d05ea94' class='xr-var-data-in' type='checkbox'><label for='data-abad8101-f58c-4de4-8335-4f583d05ea94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-7caf6c23-1d0a-44cf-af76-224ea3c7075f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7caf6c23-1d0a-44cf-af76-224ea3c7075f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31ef250a-79f4-4b93-94b2-ef1a312222ed' class='xr-var-data-in' type='checkbox'><label for='data-31ef250a-79f4-4b93-94b2-ef1a312222ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1854-01-01 ... 2021-12-01</div><input id='attrs-495bea7c-64ba-48a3-ac7b-6e755353c777' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-495bea7c-64ba-48a3-ac7b-6e755353c777' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b676752b-daf0-41e9-8d7a-20a10ff29489' class='xr-var-data-in' type='checkbox'><label for='data-b676752b-daf0-41e9-8d7a-20a10ff29489' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81053.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1854-01-01T00:00:00.000000000&#x27;, &#x27;1854-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1854-03-01T00:00:00.000000000&#x27;, ..., &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba78a63d-779f-491f-a330-dfe8fb6f9931' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ba78a63d-779f-491f-a330-dfe8fb6f9931' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nbnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f16f18dc-6d3b-40f8-bf5c-f7ac559b50d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f16f18dc-6d3b-40f8-bf5c-f7ac559b50d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-599da55c-6906-4449-b375-81d0d8699cd9' class='xr-var-data-in' type='checkbox'><label for='data-599da55c-6906-4449-b375-81d0d8699cd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time Boundaries</dd></dl></div><div class='xr-var-data'><pre>array([[9.96921e+36, 9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36],\n",
       "       ...,\n",
       "       [9.96921e+36, 9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36],\n",
       "       [9.96921e+36, 9.96921e+36]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d9c686a1-13d9-4204-a191-5ae18827079e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9c686a1-13d9-4204-a191-5ae18827079e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-758c62b3-2440-455f-b201-39a211741325' class='xr-var-data-in' type='checkbox'><label for='data-758c62b3-2440-455f-b201-39a211741325' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd></dl></div><div class='xr-var-data'><pre>[32296320 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-24090dda-ac78-4d92-b919-e215867c2035' class='xr-section-summary-in' type='checkbox'  ><label for='section-24090dda-ac78-4d92-b919-e215867c2035' class='xr-section-summary' >Attributes: <span>(37)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.</dd><dt><span>description :</span></dt><dd>In situ data: ICOADS2.5 before 2007 and NCEP in situ data from 2008 to present. Ice data: HadISST ice before 2010 and NCEP ice after 2010.</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature &gt;</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>source_comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker</dd><dt><span>geospatial_lon_min :</span></dt><dd>-1.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.0</dd><dt><span>geospatial_laty_max :</span></dt><dd>89.0</dd><dt><span>geospatial_laty_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>project :</span></dt><dd>NOAA Extended Reconstructed Sea Surface Temperature (ERSST)</dd><dt><span>original_publisher_url :</span></dt><dd>http://www.ncdc.noaa.gov</dd><dt><span>References :</span></dt><dd>https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5 at NCEI and http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html</dd><dt><span>source :</span></dt><dd>In situ data: ICOADS R3.0 before 2015, NCEP in situ GTS from 2016 to present, and Argo SST from 1999 to present. Ice data: HadISST2 ice before 2015, and NCEP ice after 2015</dd><dt><span>title :</span></dt><dd>NOAA ERSSTv5 (in situ only)</dd><dt><span>history :</span></dt><dd>created 07/2017 by PSD data using NCEI&#x27;s ERSST V5 NetCDF values</dd><dt><span>institution :</span></dt><dd>This version written at NOAA/ESRL PSD: obtained from NOAA/NESDIS/National Centers for Environmental Information and time aggregated. Original Full Source: NOAA/NESDIS/NCEI/CCOG</dd><dt><span>citation :</span></dt><dd>Huang et al, 2017: Extended Reconstructed Sea Surface Temperatures Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, https://doi.org/10.1175/JCLI-D-16-0836.1</dd><dt><span>platform :</span></dt><dd>Ship and Buoy SSTs from ICOADS R3.0 and NCEP GTS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>metadata_link :</span></dt><dd>:metadata_link = https://doi.org/10.7289/V5T72FNM (original format)</dd><dt><span>creator_name :</span></dt><dd>Boyin Huang (original)</dd><dt><span>date_created :</span></dt><dd>2017-06-30T12:18:00Z (original)</dd><dt><span>product_version :</span></dt><dd>Version 5</dd><dt><span>creator_url_original :</span></dt><dd>https://www.ncei.noaa.gov</dd><dt><span>license :</span></dt><dd>No constraints on data access or use</dd><dt><span>comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters</dd><dt><span>summary :</span></dt><dd>ERSST.v5 is developed based on v4 after revisions of 8 parameters using updated data sets and advanced knowledge of ERSST analysis</dd><dt><span>dataset_title :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>data_modified :</span></dt><dd>2022-01-07</dd></dl></div></li></ul></div></div>"
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       "<xarray.Dataset>\n",
       "Dimensions:    (lat: 89, lon: 180, time: 2016, nbnds: 2)\n",
       "Coordinates:\n",
       "  * lat        (lat) float32 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon        (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0\n",
       "  * time       (time) datetime64[ns] 1854-01-01 1854-02-01 ... 2021-12-01\n",
       "Dimensions without coordinates: nbnds\n",
       "Data variables:\n",
       "    time_bnds  (time, nbnds) float64 ...\n",
       "    sst        (time, lat, lon) float32 ...\n",
       "Attributes: (12/37)\n",
       "    climatology:               Climatology is based on 1971-2000 SST, Xue, Y....\n",
       "    description:               In situ data: ICOADS2.5 before 2007 and NCEP i...\n",
       "    keywords_vocabulary:       NASA Global Change Master Directory (GCMD) Sci...\n",
       "    keywords:                  Earth Science > Oceans > Ocean Temperature > S...\n",
       "    instrument:                Conventional thermometers\n",
       "    source_comment:            SSTs were observed by conventional thermometer...\n",
       "    ...                        ...\n",
       "    creator_url_original:      https://www.ncei.noaa.gov\n",
       "    license:                   No constraints on data access or use\n",
       "    comment:                   SSTs were observed by conventional thermometer...\n",
       "    summary:                   ERSST.v5 is developed based on v4 after revisi...\n",
       "    dataset_title:             NOAA Extended Reconstructed SST V5\n",
       "    data_modified:             2022-01-07"
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     "execution_count": 2,
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    "ds = xr.open_dataset('../../data/sst.mnmean.nc')\n",
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    "# 第一问"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 498, lat: 89, lon: 180)&gt;\n",
       "[7977960 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1855-12-01 1856-01-01 ... 2021-02-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 498</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b7ae97e7-d169-4982-be69-7144458a4a87' class='xr-array-in' type='checkbox' checked><label for='section-b7ae97e7-d169-4982-be69-7144458a4a87' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[7977960 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-897c7120-36ce-4061-9e04-5fea69100e05' class='xr-section-summary-in' type='checkbox'  checked><label for='section-897c7120-36ce-4061-9e04-5fea69100e05' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-13b92869-d79c-40ae-9529-ebb4c03b3990' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13b92869-d79c-40ae-9529-ebb4c03b3990' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-644d1746-9af5-4286-bee4-a31200192d13' class='xr-var-data-in' type='checkbox'><label for='data-644d1746-9af5-4286-bee4-a31200192d13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-5e35bdbe-b37d-4342-8482-6ae37bd029f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e35bdbe-b37d-4342-8482-6ae37bd029f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-225ba6fc-1e81-4413-9b88-30cf3c96324f' class='xr-var-data-in' type='checkbox'><label for='data-225ba6fc-1e81-4413-9b88-30cf3c96324f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1855-12-01 ... 2021-02-01</div><input id='attrs-9efefcbe-7af6-4907-820d-f5e743c62794' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9efefcbe-7af6-4907-820d-f5e743c62794' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b22f7fa2-c0e2-4f68-b244-befa3e6b2a8c' class='xr-var-data-in' type='checkbox'><label for='data-b22f7fa2-c0e2-4f68-b244-befa3e6b2a8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81053.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1855-12-01T00:00:00.000000000&#x27;, &#x27;1856-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1856-02-01T00:00:00.000000000&#x27;, ..., &#x27;2020-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-01-01T00:00:00.000000000&#x27;, &#x27;2021-02-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-02b895fe-159c-4ad8-9e8d-8cdbb18d9700' class='xr-section-summary-in' type='checkbox'  checked><label for='section-02b895fe-159c-4ad8-9e8d-8cdbb18d9700' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (time: 498, lat: 89, lon: 180)>\n",
       "[7977960 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1855-12-01 1856-01-01 ... 2021-02-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Twinter=ds['sst'].loc[ds.time.dt.season.isin(['DJF'])].loc['1855-12':'2021-03']\n",
    "Twinter"
   ]
  },
  {
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   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lat=Twinter['lat'].data\n",
    "lon=Twinter['lon'].data\n",
    "time=pd.date_range('1856','2022',freq='1Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "sstwin=np.zeros((len(time),len(lat),len(lon)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "count=0\n",
    "temp=np.zeros((len(lat), len(lon)))\n",
    "t_i=0\n",
    "for i in list(Twinter.time.data):\n",
    "    temp=temp+Twinter.loc[i,:,:]\n",
    "    count+=1\n",
    "    if count==3:\n",
    "        sstwin[t_i,:,:]=temp/3\n",
    "        count=0\n",
    "        t_i+=1\n",
    "        temp=np.zeros((len(lat), len(lon)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "dssstwin=xr.Dataset({'sstwin':(['time','lat','lon'],sstwin)},\n",
    "                    coords={'lon': (['lon'], lon),\n",
    "                           'lat': (['lat'], lat),\n",
    "                           'time': (['time'], time)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 166, lat: 89, lon: 180)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * time     (time) datetime64[ns] 1856-12-31 1857-12-31 ... 2021-12-31\n",
       "Data variables:\n",
       "    sstwin   (time, lat, lon) float64 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-71085e5a-11a9-41bd-a55b-39899234613d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-71085e5a-11a9-41bd-a55b-39899234613d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 166</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8e55f42a-9c62-4976-9684-4a4eea9c3d9b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8e55f42a-9c62-4976-9684-4a4eea9c3d9b' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-60ba5a46-82f8-4921-8831-22dafa37b222' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-60ba5a46-82f8-4921-8831-22dafa37b222' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59619f99-e6a4-41ea-9b05-9d4e157c545f' class='xr-var-data-in' type='checkbox'><label for='data-59619f99-e6a4-41ea-9b05-9d4e157c545f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-42029282-d976-4951-8e88-2e74a43f24db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42029282-d976-4951-8e88-2e74a43f24db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b82502c-5315-4a51-83cc-662fe595f72f' class='xr-var-data-in' type='checkbox'><label for='data-3b82502c-5315-4a51-83cc-662fe595f72f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1856-12-31 ... 2021-12-31</div><input id='attrs-d13cef4d-b393-41c2-8818-d93a1660ba64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d13cef4d-b393-41c2-8818-d93a1660ba64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9539eb2-7b6a-42fc-b118-ad214364e7ab' class='xr-var-data-in' type='checkbox'><label for='data-c9539eb2-7b6a-42fc-b118-ad214364e7ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1856-12-31T00:00:00.000000000&#x27;, &#x27;1857-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1858-12-31T00:00:00.000000000&#x27;, &#x27;1859-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1860-12-31T00:00:00.000000000&#x27;, &#x27;1861-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1862-12-31T00:00:00.000000000&#x27;, &#x27;1863-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1864-12-31T00:00:00.000000000&#x27;, &#x27;1865-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1866-12-31T00:00:00.000000000&#x27;, &#x27;1867-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1868-12-31T00:00:00.000000000&#x27;, &#x27;1869-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1870-12-31T00:00:00.000000000&#x27;, &#x27;1871-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1872-12-31T00:00:00.000000000&#x27;, &#x27;1873-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1874-12-31T00:00:00.000000000&#x27;, &#x27;1875-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1876-12-31T00:00:00.000000000&#x27;, &#x27;1877-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1878-12-31T00:00:00.000000000&#x27;, &#x27;1879-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1880-12-31T00:00:00.000000000&#x27;, &#x27;1881-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1882-12-31T00:00:00.000000000&#x27;, &#x27;1883-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1884-12-31T00:00:00.000000000&#x27;, &#x27;1885-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1886-12-31T00:00:00.000000000&#x27;, &#x27;1887-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1888-12-31T00:00:00.000000000&#x27;, &#x27;1889-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1890-12-31T00:00:00.000000000&#x27;, &#x27;1891-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1892-12-31T00:00:00.000000000&#x27;, &#x27;1893-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1894-12-31T00:00:00.000000000&#x27;, &#x27;1895-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1896-12-31T00:00:00.000000000&#x27;, &#x27;1897-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1898-12-31T00:00:00.000000000&#x27;, &#x27;1899-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1900-12-31T00:00:00.000000000&#x27;, &#x27;1901-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1902-12-31T00:00:00.000000000&#x27;, &#x27;1903-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1904-12-31T00:00:00.000000000&#x27;, &#x27;1905-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1906-12-31T00:00:00.000000000&#x27;, &#x27;1907-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1908-12-31T00:00:00.000000000&#x27;, &#x27;1909-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1910-12-31T00:00:00.000000000&#x27;, &#x27;1911-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1912-12-31T00:00:00.000000000&#x27;, &#x27;1913-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1914-12-31T00:00:00.000000000&#x27;, &#x27;1915-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1916-12-31T00:00:00.000000000&#x27;, &#x27;1917-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;1918-12-31T00:00:00.000000000&#x27;, &#x27;1919-12-31T00:00:00.000000000&#x27;,\n",
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       "       &#x27;1922-12-31T00:00:00.000000000&#x27;, &#x27;1923-12-31T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2010-12-31T00:00:00.000000000&#x27;, &#x27;2011-12-31T00:00:00.000000000&#x27;,\n",
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       "       &#x27;2018-12-31T00:00:00.000000000&#x27;, &#x27;2019-12-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T00:00:00.000000000&#x27;, &#x27;2021-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7fad970-b5ca-46f2-ad5b-66ea35ab8b8d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e7fad970-b5ca-46f2-ad5b-66ea35ab8b8d' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sstwin</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.8 -1.8 -1.8 -1.8 ... nan nan nan</div><input id='attrs-120ad9fb-10b1-4688-ab0a-61a305ceca94' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-120ad9fb-10b1-4688-ab0a-61a305ceca94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ec0e89b-6178-478b-992a-095de469d1f5' class='xr-var-data-in' type='checkbox'><label for='data-1ec0e89b-6178-478b-992a-095de469d1f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-1.79999995, -1.79999995, -1.79999995, ..., -1.79999995,\n",
       "         -1.79999995, -1.79999995],\n",
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       "        [-1.79999995, -1.79999995, -1.79999995, ..., -1.79999995,\n",
       "         -1.79999995, -1.79999995],\n",
       "        ...,\n",
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       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
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       "         -1.79999995, -1.79999995],\n",
       "        [-1.79999995, -1.79999995, -1.79999995, ..., -1.79999995,\n",
       "         -1.79999995, -1.79999995],\n",
       "...\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]],\n",
       "\n",
       "       [[-1.79999995, -1.79999995, -1.79999995, ..., -1.79999995,\n",
       "         -1.79999995, -1.79999995],\n",
       "        [-1.79999995, -1.79999995, -1.79999995, ..., -1.79999995,\n",
       "         -1.79999995, -1.79999995],\n",
       "        [-1.79999995, -1.79999995, -1.79999995, ..., -1.79999995,\n",
       "         -1.79999995, -1.79999995],\n",
       "        ...,\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d8bc8570-5a9a-4874-89a7-d6ac7fe6fd7f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d8bc8570-5a9a-4874-89a7-d6ac7fe6fd7f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
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       "<xarray.Dataset>\n",
       "Dimensions:  (time: 166, lat: 89, lon: 180)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
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       "Data variables:\n",
       "    sstwin   (time, lat, lon) float64 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "dssstwin"
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  },
  {
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   "metadata": {
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    "# 第二问"
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  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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    "pycharm": {
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       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 888, lat: 31, lon: 41)&gt;\n",
       "[1128648 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 30.0 28.0 26.0 24.0 22.0 ... -24.0 -26.0 -28.0 -30.0\n",
       "  * lon      (lon) float32 190.0 192.0 194.0 196.0 ... 264.0 266.0 268.0 270.0\n",
       "  * time     (time) datetime64[ns] 1948-01-01 1948-02-01 ... 2021-12-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 888</li><li><span class='xr-has-index'>lat</span>: 31</li><li><span class='xr-has-index'>lon</span>: 41</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-962035b3-f48d-4cf7-bed1-2f3cb764c398' class='xr-array-in' type='checkbox' checked><label for='section-962035b3-f48d-4cf7-bed1-2f3cb764c398' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[1128648 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-4ae025f9-27ac-49aa-9034-cdec64ada875' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4ae025f9-27ac-49aa-9034-cdec64ada875' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>30.0 28.0 26.0 ... -28.0 -30.0</div><input id='attrs-ea39767d-ac7b-439e-9f19-98e4aabcc304' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea39767d-ac7b-439e-9f19-98e4aabcc304' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fba779de-e630-4c9f-8c12-67e7be6e96b8' class='xr-var-data-in' type='checkbox'><label for='data-fba779de-e630-4c9f-8c12-67e7be6e96b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 30.,  28.,  26.,  24.,  22.,  20.,  18.,  16.,  14.,  12.,  10.,   8.,\n",
       "         6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,  -8., -10., -12., -14., -16.,\n",
       "       -18., -20., -22., -24., -26., -28., -30.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>190.0 192.0 194.0 ... 268.0 270.0</div><input id='attrs-9a9e9c12-b0a1-4493-a9e8-1b4db8e5f8a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a9e9c12-b0a1-4493-a9e8-1b4db8e5f8a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db09b448-28bb-4093-a038-eae4e1b7ccd3' class='xr-var-data-in' type='checkbox'><label for='data-db09b448-28bb-4093-a038-eae4e1b7ccd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([190., 192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212.,\n",
       "       214., 216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236.,\n",
       "       238., 240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260.,\n",
       "       262., 264., 266., 268., 270.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1948-01-01 ... 2021-12-01</div><input id='attrs-e6a15a99-a8d0-408b-8a87-0069289430dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6a15a99-a8d0-408b-8a87-0069289430dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db3e902f-a4f8-485f-8bd4-2da45d4f3d17' class='xr-var-data-in' type='checkbox'><label for='data-db3e902f-a4f8-485f-8bd4-2da45d4f3d17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81053.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1948-01-01T00:00:00.000000000&#x27;, &#x27;1948-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1948-03-01T00:00:00.000000000&#x27;, ..., &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1e0772cc-8eb3-4e6b-9c2f-47436d19f2c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1e0772cc-8eb3-4e6b-9c2f-47436d19f2c4' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (time: 888, lat: 31, lon: 41)>\n",
       "[1128648 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 30.0 28.0 26.0 24.0 22.0 ... -24.0 -26.0 -28.0 -30.0\n",
       "  * lon      (lon) float32 190.0 192.0 194.0 196.0 ... 264.0 266.0 268.0 270.0\n",
       "  * time     (time) datetime64[ns] 1948-01-01 1948-02-01 ... 2021-12-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ZDsst=ds['sst'].loc['1948':'2021',30:-30,190:270]\n",
    "ZDsst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "ZDeof=Eof(np.array(ZDsst))\n",
    "## 取前2两个模态\n",
    "eofZD=ZDeof.eofsAsCorrelation(neofs=1) # 空间场\n",
    "pcZD=ZDeof.pcs(npcs=1,pcscaling=1) # 时间序列\n",
    "evar=ZDeof.varianceFraction(neigs=1) # 解释方差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lon=ZDsst['lon'].data\n",
    "lat=ZDsst['lat'].data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 创建地图\n",
    "def createmap(ax1,lons,lone,lats,late):\n",
    "    # 海岸线\n",
    "    ax1.coastlines('110m')\n",
    "    ax1.set_extent([lons,lone,lats,late],crs=ccrs.PlateCarree())\n",
    "    # 标注坐标轴\n",
    "    ax1.set_xticks(np.arange(lons,lone+10, 10), crs=ccrs.PlateCarree())\n",
    "    ax1.set_yticks(np.arange(lats,late+10, 10), crs=ccrs.PlateCarree())\n",
    "    # 设置大小刻度\n",
    "    minorticks = MultipleLocator(10)\n",
    "    majorticks = MultipleLocator(30)\n",
    "    ax1.xaxis.set_major_locator(majorticks)\n",
    "    ax1.xaxis.set_minor_locator(minorticks)\n",
    "    ax1.yaxis.set_minor_locator(minorticks)\n",
    "    # 经纬度格式，把0经度设置不加E和W\n",
    "    lon_formatter = LongitudeFormatter(zero_direction_label=False)\n",
    "    lat_formatter = LatitudeFormatter()\n",
    "    ax1.xaxis.set_major_formatter(lon_formatter)\n",
    "    ax1.yaxis.set_major_formatter(lat_formatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(16,6))\n",
    "ax1=fig.add_axes([0.1,0.1,0.3,0.8],projection=ccrs.PlateCarree(central_longitude=226))\n",
    "createmap(ax1,190,270,-30,30)\n",
    "colorbar=ax1.contourf(lon,lat,eofZD[0,:,:],cmap=plt.cm.RdBu_r,zorder=0,extend='both',levels=np.arange(-0.9,1,0.01),transform=ccrs.PlateCarree())\n",
    "plt.colorbar(colorbar, extendrect='True', pad=0.03, fraction=0.04, shrink=0.7)\n",
    "# 设置标题\n",
    "ax1.set_title('(a) EOF1',loc='left')\n",
    "ax1.set_title(str(round(evar[0]*100,2))+'%',loc='right')\n",
    "#-------------------------------------------------------------------#\n",
    "def smoothyear(pcZD):\n",
    "    year=np.arange(1948,2022,1)\n",
    "    pc=np.zeros(len(year))\n",
    "    count=0\n",
    "    temp=0\n",
    "    t_i=0\n",
    "    for i in range(len(pcZD)):\n",
    "        temp=temp+pcZD[i,0]\n",
    "        count+=1\n",
    "        if count==12:\n",
    "            pc[t_i]=temp/12\n",
    "            count=0\n",
    "            t_i+=1\n",
    "            temp=0\n",
    "    return pc,year\n",
    "pc,year=smoothyear(pcZD)\n",
    "ax2=fig.add_axes([0.45,0.1,0.5,0.8])\n",
    "ax2.plot(year,pc)\n",
    "ax2.axhline(y=0,linestyle='--')\n",
    "ax2.set_title('(b)PC1')\n",
    "\n",
    "# 保存图片\n",
    "plt.savefig('../../picture/Pythonhome/12/ex2.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# 第三问"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 888, lat: 26, lon: 38)&gt;\n",
       "[877344 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 20.0 18.0 16.0 14.0 12.0 ... -24.0 -26.0 -28.0 -30.0\n",
       "  * lon      (lon) float32 46.0 48.0 50.0 52.0 54.0 ... 114.0 116.0 118.0 120.0\n",
       "  * time     (time) datetime64[ns] 1948-01-01 1948-02-01 ... 2021-12-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 888</li><li><span class='xr-has-index'>lat</span>: 26</li><li><span class='xr-has-index'>lon</span>: 38</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-46362844-0504-46bb-823b-1406122e830e' class='xr-array-in' type='checkbox' checked><label for='section-46362844-0504-46bb-823b-1406122e830e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[877344 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-c519765e-4ecf-450d-839a-123091ac9034' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c519765e-4ecf-450d-839a-123091ac9034' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.0 18.0 16.0 ... -28.0 -30.0</div><input id='attrs-2f2b6161-526d-4391-8f7f-15b7d6ecac37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f2b6161-526d-4391-8f7f-15b7d6ecac37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a1d667f-46c1-4652-a7d2-b921ee3c32a9' class='xr-var-data-in' type='checkbox'><label for='data-7a1d667f-46c1-4652-a7d2-b921ee3c32a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 20.,  18.,  16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,\n",
       "        -4.,  -6.,  -8., -10., -12., -14., -16., -18., -20., -22., -24., -26.,\n",
       "       -28., -30.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>46.0 48.0 50.0 ... 118.0 120.0</div><input id='attrs-1c0c14a5-29cc-4562-b8c1-2e7c3ab6799d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c0c14a5-29cc-4562-b8c1-2e7c3ab6799d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d4ee0b4-f6fa-437b-a8d5-1138afab1d16' class='xr-var-data-in' type='checkbox'><label for='data-9d4ee0b4-f6fa-437b-a8d5-1138afab1d16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 46.,  48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,\n",
       "        70.,  72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,\n",
       "        94.,  96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116.,\n",
       "       118., 120.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1948-01-01 ... 2021-12-01</div><input id='attrs-62ed7c31-6a80-4475-a958-c49cd2c2325a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62ed7c31-6a80-4475-a958-c49cd2c2325a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08fdf75f-6aab-49fb-82df-855de19332fa' class='xr-var-data-in' type='checkbox'><label for='data-08fdf75f-6aab-49fb-82df-855de19332fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81053.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1948-01-01T00:00:00.000000000&#x27;, &#x27;1948-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1948-03-01T00:00:00.000000000&#x27;, ..., &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c7a8fc47-d692-461c-b9e9-20b0d9aabb95' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c7a8fc47-d692-461c-b9e9-20b0d9aabb95' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (time: 888, lat: 26, lon: 38)>\n",
       "[877344 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 20.0 18.0 16.0 14.0 12.0 ... -24.0 -26.0 -28.0 -30.0\n",
       "  * lon      (lon) float32 46.0 48.0 50.0 52.0 54.0 ... 114.0 116.0 118.0 120.0\n",
       "  * time     (time) datetime64[ns] 1948-01-01 1948-02-01 ... 2021-12-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YDsst=ds['sst'].loc['1948':'2021',20:-30,45:120]\n",
    "YDsst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "YDeof=Eof(np.array(YDsst))\n",
    "## 取前2两个模态\n",
    "eofYD=YDeof.eofsAsCorrelation(neofs=1) # 空间场\n",
    "pcYD=YDeof.pcs(npcs=1,pcscaling=1) # 时间序列\n",
    "evarYD=YDeof.varianceFraction(neigs=1) # 解释方差\n",
    "#-----------------------------------------------#\n",
    "lon=YDsst['lon'].data\n",
    "lat=YDsst['lat'].data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(16,6))\n",
    "ax1=fig.add_axes([0.1,0.1,0.3,0.8],projection=ccrs.PlateCarree(central_longitude=226))\n",
    "createmap(ax1,46,110,-30,20)\n",
    "colorbar=ax1.contourf(lon,lat,eofYD[0,:,:],cmap=plt.cm.RdBu_r,zorder=0,extend='both',levels=np.arange(-1,1,0.01),transform=ccrs.PlateCarree())\n",
    "plt.colorbar(colorbar, extendrect='True', pad=0.03, fraction=0.04, shrink=0.7)\n",
    "# 设置标题\n",
    "ax1.set_title('(a) EOF1',loc='left')\n",
    "ax1.set_title(str(round(evarYD[0]*100,2))+'%',loc='right')\n",
    "#-------------------------------------------------------------------#\n",
    "pc1,year=smoothyear(pcYD)\n",
    "ax2=fig.add_axes([0.45,0.1,0.5,0.8])\n",
    "ax2.plot(year,pc1)\n",
    "ax2.axhline(y=0,linestyle='--')\n",
    "ax2.set_title('(b)PC1')\n",
    "\n",
    "# 保存图片\n",
    "plt.savefig('../../picture/Pythonhome/12/ex3.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# 第四问 协方差、相关系数\n",
    "$r=\\frac{\\frac{1}{n}\\sum_{i=1}^{n}(X_{i}-\\overline{X})(Y_{i}-\\overline{Y})}{\\frac{1}{n}\\sqrt{\\sum_{i=1}^{n}(X_{i}-\\overline{X})^2}\\sqrt{\\sum_{i=1}^{n}(Y_{i}-\\overline{Y})^2}}$\n",
    "<br>\n",
    "协方差\n",
    "$S_{xy}=\\frac{1}{n}\\sum_{i=1}^{n}(x_{i}-\\overline{x})(y_{i}-\\overline{y})$"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;air&#x27; (time: 888, lat: 17, lon: 27)&gt;\n",
       "[407592 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 55.0 52.5 50.0 47.5 45.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 70.0 72.5 75.0 77.5 80.0 ... 127.5 130.0 132.5 135.0\n",
       "  * time     (time) datetime64[ns] 1948-01-01 1948-02-01 ... 2021-12-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Mean Air Temperature at sigma level 0.995\n",
       "    valid_range:   [-2000.  2000.]\n",
       "    units:         degC\n",
       "    precision:     1\n",
       "    var_desc:      Air Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    parent_stat:   Individual Obs\n",
       "    dataset:       NCEP Reanalysis Derived Products\n",
       "    actual_range:  [-73.78001  42.14595]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'air'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 888</li><li><span class='xr-has-index'>lat</span>: 17</li><li><span class='xr-has-index'>lon</span>: 27</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2bd41e10-ad81-48c0-a189-b35a5180beab' class='xr-array-in' type='checkbox' checked><label for='section-2bd41e10-ad81-48c0-a189-b35a5180beab' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[407592 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-4d497c0f-35a7-47c2-b97e-314e11abf292' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4d497c0f-35a7-47c2-b97e-314e11abf292' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>55.0 52.5 50.0 ... 20.0 17.5 15.0</div><input id='attrs-74a0dd5d-1938-43ad-8700-dbb66ed94c26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74a0dd5d-1938-43ad-8700-dbb66ed94c26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a5d6582-83c4-4653-a0d0-93fbd6a122b0' class='xr-var-data-in' type='checkbox'><label for='data-2a5d6582-83c4-4653-a0d0-93fbd6a122b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>actual_range :</span></dt><dd>[ 90. -90.]</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([55. , 52.5, 50. , 47.5, 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5,\n",
       "       25. , 22.5, 20. , 17.5, 15. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>70.0 72.5 75.0 ... 132.5 135.0</div><input id='attrs-c5be8c00-4c9d-42cb-9b39-19e9ee2bb097' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5be8c00-4c9d-42cb-9b39-19e9ee2bb097' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d9d6ff6-5a4d-4ebf-b299-ab4dd251ebfa' class='xr-var-data-in' type='checkbox'><label for='data-7d9d6ff6-5a4d-4ebf-b299-ab4dd251ebfa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0.  357.5]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 70. ,  72.5,  75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,\n",
       "        95. ,  97.5, 100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5,\n",
       "       120. , 122.5, 125. , 127.5, 130. , 132.5, 135. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1948-01-01 ... 2021-12-01</div><input id='attrs-a3d3703a-3e1c-4ed1-bcc7-cb62935f9b1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3d3703a-3e1c-4ed1-bcc7-cb62935f9b1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c8eaef4-4cab-4097-9ceb-6b023829664b' class='xr-var-data-in' type='checkbox'><label for='data-5c8eaef4-4cab-4097-9ceb-6b023829664b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-01 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[1297320. 1947432.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1948-01-01T00:00:00.000000000&#x27;, &#x27;1948-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1948-03-01T00:00:00.000000000&#x27;, ..., &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bad70d5e-645c-4165-9dca-cce4f2c2030c' class='xr-section-summary-in' type='checkbox'  ><label for='section-bad70d5e-645c-4165-9dca-cce4f2c2030c' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Mean Air Temperature at sigma level 0.995</dd><dt><span>valid_range :</span></dt><dd>[-2000.  2000.]</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>precision :</span></dt><dd>1</dd><dt><span>var_desc :</span></dt><dd>Air Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>parent_stat :</span></dt><dd>Individual Obs</dd><dt><span>dataset :</span></dt><dd>NCEP Reanalysis Derived Products</dd><dt><span>actual_range :</span></dt><dd>[-73.78001  42.14595]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'air' (time: 888, lat: 17, lon: 27)>\n",
       "[407592 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 55.0 52.5 50.0 47.5 45.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 70.0 72.5 75.0 77.5 80.0 ... 127.5 130.0 132.5 135.0\n",
       "  * time     (time) datetime64[ns] 1948-01-01 1948-02-01 ... 2021-12-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Mean Air Temperature at sigma level 0.995\n",
       "    valid_range:   [-2000.  2000.]\n",
       "    units:         degC\n",
       "    precision:     1\n",
       "    var_desc:      Air Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    parent_stat:   Individual Obs\n",
       "    dataset:       NCEP Reanalysis Derived Products\n",
       "    actual_range:  [-73.78001  42.14595]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取温度\n",
    "airchina=xr.open_dataset('../../data/air.mon.mean.nc')['air'].loc['1948':'2021',55:15,70:135]\n",
    "airchina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 求相关系数\n",
    "def xiangguan(x,ds):\n",
    "    x=x-x.mean()\n",
    "    y=ds-ds.mean(dim='time')\n",
    "    x=x.reshape((len(x),1,1))\n",
    "    up=np.sum(x*y,axis=0)/len(x)\n",
    "    down=(np.sqrt(np.sum(x**2))*np.sqrt(np.sum(y**2,axis=0)))/len(x)\n",
    "    # 相关系数\n",
    "    r=up/down\n",
    "    return r\n",
    "# 求协方差\n",
    "def xiefang(x,ds):\n",
    "    x=x-x.mean()\n",
    "    y=ds-ds.mean(dim='time')\n",
    "    x=x.reshape((len(x),1,1))\n",
    "    xie=(y*x).sum(axis=0)/len(x)\n",
    "    return xie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lat=airchina.lat.data\n",
    "lon=airchina.lon.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 297 ms\n",
      "Wall time: 288 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#--------------------协方差---------------------#\n",
    "ZDxie_china=xiefang(pcZD[:,0],airchina)\n",
    "YDxie_china=xiefang(pcYD[:,0],airchina)\n",
    "#--------------------相关系数--------------------#\n",
    "ZDxiang_china=xiangguan(pcZD[:,0],airchina)\n",
    "YDxiang_china=xiangguan(pcYD[:,0],airchina)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "###################################耗时过长####################################################\n",
    "%%time\n",
    "ZDxie_china=np.zeros((len(airchina.lat),len(airchina.lon)))\n",
    "YDxie_china=np.zeros((len(airchina.lat),len(airchina.lon)))\n",
    "ZDxiang_china=np.zeros((len(airchina.lat),len(airchina.lon)))\n",
    "YDxiang_china=np.zeros((len(airchina.lat),len(airchina.lon)))\n",
    "latlist=list(airchina.lat.data)\n",
    "lonlist=list(airchina.lon.data)\n",
    "for lat_i in latlist:\n",
    "    for lon_i in lonlist:\n",
    "        #-------------------协方差-----------------------#\n",
    "        ZDxie_china[latlist.index(lat_i),lonlist.index(lon_i)]=np.cov(pcZD[:,0],airchina.loc[:,lat_i,lon_i].data)[0,1]\n",
    "        YDxie_china[latlist.index(lat_i),lonlist.index(lon_i)]=np.cov(pcYD[:,0],airchina.loc[:,lat_i,lon_i].data)[0,1]\n",
    "        #--------------------相关系数---------------------#\n",
    "        ZDxiang_china[latlist.index(lat_i),lonlist.index(lon_i)]=np.corrcoef(pcZD[:,0],airchina.loc[:,lat_i,lon_i].data)[0,1]\n",
    "        YDxiang_china[latlist.index(lat_i),lonlist.index(lon_i)]=np.corrcoef(pcYD[:,0],airchina.loc[:,lat_i,lon_i].data)[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x648 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(16,9))\n",
    "ax=fig.subplots(2,2,subplot_kw={'projection':ccrs.PlateCarree(central_longitude=115)})\n",
    "ax1=ax[0,0]\n",
    "ax2=ax[0,1]\n",
    "ax3=ax[1,0]\n",
    "ax4=ax[1,1]\n",
    "for i in [ax1,ax2,ax3,ax4]:\n",
    "    createmap(i,70,125,15,55)\n",
    "#------------------------ZD协方差-----------------------#\n",
    "colorbar=ax1.contourf(lon,lat,ZDxie_china,cmap='bwr',transform=ccrs.PlateCarree())\n",
    "plt.colorbar(colorbar,extendrect='True', pad=0.03, fraction=0.04, shrink=0.7,ax=ax1)\n",
    "ax1.set_title('1948-2021 中东太平洋第一模态时间系数跟中国气温的协方差')\n",
    "#------------------------YD协方差-----------------------#\n",
    "colorbar=ax2.contourf(lon,lat,YDxie_china,cmap='bwr',transform=ccrs.PlateCarree())\n",
    "plt.colorbar(colorbar,extendrect='True', pad=0.03, fraction=0.04, shrink=0.7,ax=ax2)\n",
    "ax2.set_title('1948-2021 印度太平洋第一模态时间系数跟中国气温的协方差')\n",
    "#-------------------------ZD相关系数---------------------#\n",
    "colorbar=ax3.contourf(lon,lat,ZDxiang_china,cmap='bwr',transform=ccrs.PlateCarree())\n",
    "plt.colorbar(colorbar,extendrect='True', pad=0.03, fraction=0.04, shrink=0.7,ax=ax3)\n",
    "ax3.set_title('1948-2021 中东太平洋第一模态时间系数跟中国气温的相关系数')\n",
    "#-------------------------YD相关系数---------------------#\n",
    "colorbar=ax4.contourf(lon,lat,YDxiang_china,cmap='bwr',transform=ccrs.PlateCarree())\n",
    "plt.colorbar(colorbar,extendrect='True', pad=0.03, fraction=0.04, shrink=0.7,ax=ax4)\n",
    "ax4.set_title('1948-2021 印度太平洋第一模态时间系数跟中国气温的相关系数')\n",
    "\n",
    "### 保存图片\n",
    "plt.savefig('../../picture/Pythonhome/12/ex4.png')"
   ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 888, lat: 31, lon: 41)&gt;\n",
       "array([[[18.999987, 18.881481, ..., 18.296137, 19.220795],\n",
       "        [20.575962, 20.38293 , ..., 20.149712, 21.147705],\n",
       "        ...,\n",
       "        [22.792307, 22.598955, ..., 23.20208 , 22.912247],\n",
       "        [21.744492, 21.573185, ..., 23.058504, 22.840729]],\n",
       "\n",
       "       [[18.683554, 18.520248, ..., 18.065914, 19.111307],\n",
       "        [20.264135, 20.062288, ..., 19.958687, 21.090076],\n",
       "        ...,\n",
       "        [23.558964, 23.39474 , ..., 23.540627, 23.236948],\n",
       "        [22.54712 , 22.40247 , ..., 23.375528, 23.12062 ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[24.681038, 24.510618, ..., 22.450792, 22.249113],\n",
       "        [25.469456, 25.333132, ..., 24.121357, 24.024864],\n",
       "        ...,\n",
       "        [22.563692, 22.505722, ..., 19.196037, 18.852955],\n",
       "        [21.209242, 21.16961 , ..., 18.754726, 18.487478]],\n",
       "\n",
       "       [[22.0043  , 21.94211 , ..., 20.905008, 20.885063],\n",
       "        [23.151243, 23.117977, ..., 22.786385, 22.877874],\n",
       "        ...,\n",
       "        [23.94654 , 23.83421 , ..., 20.369104, 20.023697],\n",
       "        [22.77168 , 22.67178 , ..., 20.156498, 19.87732 ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 30.0 28.0 26.0 24.0 22.0 ... -24.0 -26.0 -28.0 -30.0\n",
       "  * lon      (lon) float32 190.0 192.0 194.0 196.0 ... 264.0 266.0 268.0 270.0\n",
       "  * time     (time) datetime64[ns] 1948-01-01 1948-02-01 ... 2021-12-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 888</li><li><span class='xr-has-index'>lat</span>: 31</li><li><span class='xr-has-index'>lon</span>: 41</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f548ab8c-7c4c-4a49-aece-f4e2c4d6d6dd' class='xr-array-in' type='checkbox' checked><label for='section-f548ab8c-7c4c-4a49-aece-f4e2c4d6d6dd' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>19.0 18.88 18.85 18.89 18.9 18.95 ... 20.81 20.61 20.4 20.16 19.88</span></div><div class='xr-array-data'><pre>array([[[18.999987, 18.881481, ..., 18.296137, 19.220795],\n",
       "        [20.575962, 20.38293 , ..., 20.149712, 21.147705],\n",
       "        ...,\n",
       "        [22.792307, 22.598955, ..., 23.20208 , 22.912247],\n",
       "        [21.744492, 21.573185, ..., 23.058504, 22.840729]],\n",
       "\n",
       "       [[18.683554, 18.520248, ..., 18.065914, 19.111307],\n",
       "        [20.264135, 20.062288, ..., 19.958687, 21.090076],\n",
       "        ...,\n",
       "        [23.558964, 23.39474 , ..., 23.540627, 23.236948],\n",
       "        [22.54712 , 22.40247 , ..., 23.375528, 23.12062 ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[24.681038, 24.510618, ..., 22.450792, 22.249113],\n",
       "        [25.469456, 25.333132, ..., 24.121357, 24.024864],\n",
       "        ...,\n",
       "        [22.563692, 22.505722, ..., 19.196037, 18.852955],\n",
       "        [21.209242, 21.16961 , ..., 18.754726, 18.487478]],\n",
       "\n",
       "       [[22.0043  , 21.94211 , ..., 20.905008, 20.885063],\n",
       "        [23.151243, 23.117977, ..., 22.786385, 22.877874],\n",
       "        ...,\n",
       "        [23.94654 , 23.83421 , ..., 20.369104, 20.023697],\n",
       "        [22.77168 , 22.67178 , ..., 20.156498, 19.87732 ]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-ddb76f07-cb99-448b-bb45-c55878843571' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ddb76f07-cb99-448b-bb45-c55878843571' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>30.0 28.0 26.0 ... -28.0 -30.0</div><input id='attrs-f53d5bbd-67e5-46d2-a844-9fb20831dd30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f53d5bbd-67e5-46d2-a844-9fb20831dd30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-359715c6-ace0-4742-86c5-7ab8b2339efa' class='xr-var-data-in' type='checkbox'><label for='data-359715c6-ace0-4742-86c5-7ab8b2339efa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 30.,  28.,  26.,  24.,  22.,  20.,  18.,  16.,  14.,  12.,  10.,   8.,\n",
       "         6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,  -8., -10., -12., -14., -16.,\n",
       "       -18., -20., -22., -24., -26., -28., -30.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>190.0 192.0 194.0 ... 268.0 270.0</div><input id='attrs-32f6958c-7e01-416c-bd24-06944f92d1da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32f6958c-7e01-416c-bd24-06944f92d1da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b53e3f0c-71a4-4fd5-b747-178ac2aacf23' class='xr-var-data-in' type='checkbox'><label for='data-b53e3f0c-71a4-4fd5-b747-178ac2aacf23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([190., 192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212.,\n",
       "       214., 216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236.,\n",
       "       238., 240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260.,\n",
       "       262., 264., 266., 268., 270.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1948-01-01 ... 2021-12-01</div><input id='attrs-1d64ab2b-a9b3-43b0-bb72-17e87a40ef87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d64ab2b-a9b3-43b0-bb72-17e87a40ef87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9d6baf4-531c-4e26-9912-5f8970bf2649' class='xr-var-data-in' type='checkbox'><label for='data-c9d6baf4-531c-4e26-9912-5f8970bf2649' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81053.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1948-01-01T00:00:00.000000000&#x27;, &#x27;1948-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1948-03-01T00:00:00.000000000&#x27;, ..., &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-226ae8ff-248d-4e70-8014-3dbe717fe368' class='xr-section-summary-in' type='checkbox'  checked><label for='section-226ae8ff-248d-4e70-8014-3dbe717fe368' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd></dl></div></li></ul></div></div>"
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       "        [20.575962, 20.38293 , ..., 20.149712, 21.147705],\n",
       "        ...,\n",
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       "\n",
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       "        ...,\n",
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       "        [22.54712 , 22.40247 , ..., 23.375528, 23.12062 ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[24.681038, 24.510618, ..., 22.450792, 22.249113],\n",
       "        [25.469456, 25.333132, ..., 24.121357, 24.024864],\n",
       "        ...,\n",
       "        [22.563692, 22.505722, ..., 19.196037, 18.852955],\n",
       "        [21.209242, 21.16961 , ..., 18.754726, 18.487478]],\n",
       "\n",
       "       [[22.0043  , 21.94211 , ..., 20.905008, 20.885063],\n",
       "        [23.151243, 23.117977, ..., 22.786385, 22.877874],\n",
       "        ...,\n",
       "        [23.94654 , 23.83421 , ..., 20.369104, 20.023697],\n",
       "        [22.77168 , 22.67178 , ..., 20.156498, 19.87732 ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 30.0 28.0 26.0 24.0 22.0 ... -24.0 -26.0 -28.0 -30.0\n",
       "  * lon      (lon) float32 190.0 192.0 194.0 196.0 ... 264.0 266.0 268.0 270.0\n",
       "  * time     (time) datetime64[ns] 1948-01-01 1948-02-01 ... 2021-12-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Means of Sea Surface Temperature\n",
       "    units:         degC\n",
       "    var_desc:      Sea Surface Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    dataset:       NOAA Extended Reconstructed SST V5\n",
       "    parent_stat:   Individual Values\n",
       "    actual_range:  [-1.8     42.32636]\n",
       "    valid_range:   [-1.8 45. ]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ZDsst"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "airchina['time']=ZDsst['time']"
   ]
  },
  {
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   "execution_count": 24,
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       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;air&#x27; (time: 888, lat: 17, lon: 27)&gt;\n",
       "array([[[-11.106443, -10.504189, ..., -32.511288, -28.634514],\n",
       "        [-10.710965, -10.459991, ..., -29.579998, -27.843546],\n",
       "        ...,\n",
       "        [ 24.483873,  25.349678, ...,  23.959682,  24.41968 ],\n",
       "        [ 24.88903 ,  26.513866, ...,  25.283545,  25.283556]],\n",
       "\n",
       "       [[-12.873443, -12.232409, ..., -26.10862 , -23.263449],\n",
       "        [-12.23241 , -12.239993, ..., -21.616896, -21.184822],\n",
       "        ...,\n",
       "        [ 24.530693,  26.177586, ...,  24.401726,  24.332764],\n",
       "        [ 24.703102,  26.316896, ...,  25.397238,  24.91345 ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ -7.30501 ,  -6.575844, ..., -16.709177, -13.472514],\n",
       "        [ -7.52501 ,  -6.977514, ..., -12.451676, -11.879181],\n",
       "        ...,\n",
       "        [ 27.876654,  27.005817, ...,  27.268324,  27.422487],\n",
       "        [ 27.432486,  27.781656, ...,  27.807487,  27.503319]],\n",
       "\n",
       "       [[-10.886298, -10.002423, ..., -30.638721, -27.683079],\n",
       "        [-11.255654, -10.657269, ..., -26.111303, -26.38469 ],\n",
       "        ...,\n",
       "        [ 25.901602,  26.100792, ...,  25.16854 ,  25.580633],\n",
       "        [ 26.30725 ,  27.41208 , ...,  26.38063 ,  26.353209]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 55.0 52.5 50.0 47.5 45.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 70.0 72.5 75.0 77.5 80.0 ... 127.5 130.0 132.5 135.0\n",
       "  * time     (time) datetime64[ns] 1948-01-01 1948-02-01 ... 2021-12-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Mean Air Temperature at sigma level 0.995\n",
       "    valid_range:   [-2000.  2000.]\n",
       "    units:         degC\n",
       "    precision:     1\n",
       "    var_desc:      Air Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    parent_stat:   Individual Obs\n",
       "    dataset:       NCEP Reanalysis Derived Products\n",
       "    actual_range:  [-73.78001  42.14595]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'air'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 888</li><li><span class='xr-has-index'>lat</span>: 17</li><li><span class='xr-has-index'>lon</span>: 27</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d585fccf-688f-47c0-9e69-4e62a2be5de8' class='xr-array-in' type='checkbox' checked><label for='section-d585fccf-688f-47c0-9e69-4e62a2be5de8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-11.11 -10.5 -10.87 -12.79 -14.23 ... 25.73 26.12 25.68 26.38 26.35</span></div><div class='xr-array-data'><pre>array([[[-11.106443, -10.504189, ..., -32.511288, -28.634514],\n",
       "        [-10.710965, -10.459991, ..., -29.579998, -27.843546],\n",
       "        ...,\n",
       "        [ 24.483873,  25.349678, ...,  23.959682,  24.41968 ],\n",
       "        [ 24.88903 ,  26.513866, ...,  25.283545,  25.283556]],\n",
       "\n",
       "       [[-12.873443, -12.232409, ..., -26.10862 , -23.263449],\n",
       "        [-12.23241 , -12.239993, ..., -21.616896, -21.184822],\n",
       "        ...,\n",
       "        [ 24.530693,  26.177586, ...,  24.401726,  24.332764],\n",
       "        [ 24.703102,  26.316896, ...,  25.397238,  24.91345 ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ -7.30501 ,  -6.575844, ..., -16.709177, -13.472514],\n",
       "        [ -7.52501 ,  -6.977514, ..., -12.451676, -11.879181],\n",
       "        ...,\n",
       "        [ 27.876654,  27.005817, ...,  27.268324,  27.422487],\n",
       "        [ 27.432486,  27.781656, ...,  27.807487,  27.503319]],\n",
       "\n",
       "       [[-10.886298, -10.002423, ..., -30.638721, -27.683079],\n",
       "        [-11.255654, -10.657269, ..., -26.111303, -26.38469 ],\n",
       "        ...,\n",
       "        [ 25.901602,  26.100792, ...,  25.16854 ,  25.580633],\n",
       "        [ 26.30725 ,  27.41208 , ...,  26.38063 ,  26.353209]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-ec372c60-4921-4366-bce2-01ab4562d571' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ec372c60-4921-4366-bce2-01ab4562d571' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>55.0 52.5 50.0 ... 20.0 17.5 15.0</div><input id='attrs-57f6b578-42e7-4c39-86ab-7ce7e2ef04f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57f6b578-42e7-4c39-86ab-7ce7e2ef04f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-292ef7bb-adc0-4ea2-bc9f-ff7ea7992b9c' class='xr-var-data-in' type='checkbox'><label for='data-292ef7bb-adc0-4ea2-bc9f-ff7ea7992b9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>actual_range :</span></dt><dd>[ 90. -90.]</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([55. , 52.5, 50. , 47.5, 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5,\n",
       "       25. , 22.5, 20. , 17.5, 15. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>70.0 72.5 75.0 ... 132.5 135.0</div><input id='attrs-a7bcce61-41a7-4d58-9556-9aca9e6ad159' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7bcce61-41a7-4d58-9556-9aca9e6ad159' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7de337ad-143e-44b4-8fc3-4066a53e5d19' class='xr-var-data-in' type='checkbox'><label for='data-7de337ad-143e-44b4-8fc3-4066a53e5d19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0.  357.5]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 70. ,  72.5,  75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,\n",
       "        95. ,  97.5, 100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5,\n",
       "       120. , 122.5, 125. , 127.5, 130. , 132.5, 135. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1948-01-01 ... 2021-12-01</div><input id='attrs-512c0246-4fc6-4e45-9ea4-71600b30d1ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-512c0246-4fc6-4e45-9ea4-71600b30d1ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4399d69a-5e75-4bb1-b8a8-24c56f31df8a' class='xr-var-data-in' type='checkbox'><label for='data-4399d69a-5e75-4bb1-b8a8-24c56f31df8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81053.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1948-01-01T00:00:00.000000000&#x27;, &#x27;1948-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1948-03-01T00:00:00.000000000&#x27;, ..., &#x27;2021-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-11-01T00:00:00.000000000&#x27;, &#x27;2021-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ee183655-2fd2-4a81-a59c-34c0c87a78b8' class='xr-section-summary-in' type='checkbox'  ><label for='section-ee183655-2fd2-4a81-a59c-34c0c87a78b8' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Mean Air Temperature at sigma level 0.995</dd><dt><span>valid_range :</span></dt><dd>[-2000.  2000.]</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>precision :</span></dt><dd>1</dd><dt><span>var_desc :</span></dt><dd>Air Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>parent_stat :</span></dt><dd>Individual Obs</dd><dt><span>dataset :</span></dt><dd>NCEP Reanalysis Derived Products</dd><dt><span>actual_range :</span></dt><dd>[-73.78001  42.14595]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'air' (time: 888, lat: 17, lon: 27)>\n",
       "array([[[-11.106443, -10.504189, ..., -32.511288, -28.634514],\n",
       "        [-10.710965, -10.459991, ..., -29.579998, -27.843546],\n",
       "        ...,\n",
       "        [ 24.483873,  25.349678, ...,  23.959682,  24.41968 ],\n",
       "        [ 24.88903 ,  26.513866, ...,  25.283545,  25.283556]],\n",
       "\n",
       "       [[-12.873443, -12.232409, ..., -26.10862 , -23.263449],\n",
       "        [-12.23241 , -12.239993, ..., -21.616896, -21.184822],\n",
       "        ...,\n",
       "        [ 24.530693,  26.177586, ...,  24.401726,  24.332764],\n",
       "        [ 24.703102,  26.316896, ...,  25.397238,  24.91345 ]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ -7.30501 ,  -6.575844, ..., -16.709177, -13.472514],\n",
       "        [ -7.52501 ,  -6.977514, ..., -12.451676, -11.879181],\n",
       "        ...,\n",
       "        [ 27.876654,  27.005817, ...,  27.268324,  27.422487],\n",
       "        [ 27.432486,  27.781656, ...,  27.807487,  27.503319]],\n",
       "\n",
       "       [[-10.886298, -10.002423, ..., -30.638721, -27.683079],\n",
       "        [-11.255654, -10.657269, ..., -26.111303, -26.38469 ],\n",
       "        ...,\n",
       "        [ 25.901602,  26.100792, ...,  25.16854 ,  25.580633],\n",
       "        [ 26.30725 ,  27.41208 , ...,  26.38063 ,  26.353209]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 55.0 52.5 50.0 47.5 45.0 ... 25.0 22.5 20.0 17.5 15.0\n",
       "  * lon      (lon) float32 70.0 72.5 75.0 77.5 80.0 ... 127.5 130.0 132.5 135.0\n",
       "  * time     (time) datetime64[ns] 1948-01-01 1948-02-01 ... 2021-12-01\n",
       "Attributes:\n",
       "    long_name:     Monthly Mean Air Temperature at sigma level 0.995\n",
       "    valid_range:   [-2000.  2000.]\n",
       "    units:         degC\n",
       "    precision:     1\n",
       "    var_desc:      Air Temperature\n",
       "    level_desc:    Surface\n",
       "    statistic:     Mean\n",
       "    parent_stat:   Individual Obs\n",
       "    dataset:       NCEP Reanalysis Derived Products\n",
       "    actual_range:  [-73.78001  42.14595]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airchina"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from xMCA import xMCA\n",
    "#--------------------中东太平洋海温和我国气温的svd分析------------#\n",
    "ZDsvd=xMCA(airchina,ZDsst)\n",
    "ZDsvd.solver()\n",
    "lp,rp=ZDsvd.patterns(n=1)\n",
    "le,re=ZDsvd.expansionCoefs(n=1)\n",
    "frac=ZDsvd.covFracs(n=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lon=lp['lon'].data\n",
    "lat=lp['lat'].data\n",
    "lon1=rp['lon'].data\n",
    "lat1=rp['lat'].data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 对时间序列求年平均\n",
    "def smoothsvd(le):\n",
    "    year=np.arange(1948,2022,1)\n",
    "    pc=np.zeros(len(year))\n",
    "    count=0\n",
    "    temp=0\n",
    "    t_i=0\n",
    "    for i in range(len(le[0,:])):\n",
    "        temp=temp+le[0,i].data\n",
    "        count+=1\n",
    "        if count==12:\n",
    "            pc[t_i]=temp/12\n",
    "            count=0\n",
    "            t_i+=1\n",
    "            temp=0\n",
    "    return pc,year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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z4QSzptJ77T4btmjRMsNdNIwad8pydJ1d1gQu12uKbDL6mhxM4lddZMdUbKrr8Uuf/NlH/3P37XNYnaxtSseZ7zMTYBNgXMPWpuF06dLFe/bs2dDVEBERESmYCRMmTHX3rvGWpQrWo8zsUoIu3LsB/c3shajnl2VRn+uAD0gQrMNbtYwE2KD/AH/mlTey2EVpatmsCSTq0IKlxZlC5y2p32S78xbnbDLiRmv6giUZr7N9n+5d8lCVjJmZdaWSLenCd8x/B2g6f7Qxevbsyfjx6YwAEhEREWkczOyHRMtSdQW/CBgFTATeAbaIfu7uL2daGXf/EBiT6XqNWctm1qRCdTFrXZHqTyLF+pXlOapJ49WpZcWyR6nZhe41HalgNVoynSWYWaeGrpOIiIiINLykLdbu/inBzODRYp+nxd0Pjfq5KpttNDYK08UpEq6zbb1uXVmulus0RcJ1Nq3YDeFz5rA+7TCMtWlNb1pPowm3WouIiIhIoH7Nc5I1heri17qiLOsW7NaV5Wq9zkAptGCbWdfZLKUblQD0pg2TmYcmMRMRERGRVGOsJccUqEtP64oytV4XSDG3YG9Chz8qKcPCBurmlNGFSgbRsQa1WouIiIg0aWqxLhCNoy5t9Rl7rZbrzBVbC7aZlX3NPNakda3X16UNnzOngWolIiIiIsVCLdYFkG2gblmPMLegnrNbS131GXsdCddqvc5MsYTrHehW/T3zqYz5LrIrzZlHNWa2grtPaaDqiUgmEo3eyMFtF0VEpOlSsM6jhgjU8bahkJ1b6hre9HzOHAbSIe6yPrSmBy1/Q93BRURERJosdQXPk2xCdcuKspyE6kTbzce2myp1DW86zGyVhdTQOZy0LNYatOabYBIz/WJFREREmii1WOdBMY+lbllRllELdqblJT1quS4dG9L+p3ZJPiorKGNFWrAxHZaiVmsRERGRJklNmE3AgiU1tR6ZrpsPxfzlQyEoVJcGM2v2HfPpRauk5YJJzOYWqFYiIiIiUmwUrPNgwdLsJkDJRYiNDdHF3NrcVMO1QnXp+Ctdl6xMC5ql+KjsRCVLqMHMVitQ1URERESkiKgreJFZsKQm47HQxRCeI3XOtC4tm1nWX0SUIoXq0vIFcxhCp7TKrkNbVqTF96g7uIiIiEiToxbrIpROOC3WFulsJkhrqi3XpWD6giXLHk2Nma1RA3QgvVt+9aIV3zMfM0u5gpl1N7OxUc/vMLN3zOzcFOulVU5ERERECkvBukjFC8ylEqabQrjO5nZbpdRaHS9MN7WA3Y92X69Dm7TLl2OsSku2pcviZOXMrCNwD9A6fL4HUO7ug4FeZtY7wXpplRMRERGRwlOwLmKlMFY6UYjO5vZepRauM1EKoTrd1ummEK7NrPlPLKBniknLYq2T3iRm1cC+wOzweRXwSPjzi8BmCdZLt5yIiIiIFJjGWEvW0gnOul1X8YfqbILy9AVL6NQyvS7SJWq9Faw5bcoy/HKIShZVV2Nmw4HhUYtGuvtIAHefDWC27Iuk1sAv4c/TgQEJNp9uOREREREpMAXrGLFdfFtn0a0ZgpnBG3MLbCYyCdeNbTKzYg7V9W15buTh2rLtzlOGEYbokWmuMhdoGf7chsQ9idItJyIiIiIF1mSDdTZjZEtRspBan+CfcTfvIgrXkePOdh/pvncac6iO3U4jDtiFMIGgW/c4oB8wqZ7lRERERKTAmlywzjRQz1tSk3WrdUPIJCxm26qezeRkkfWKJVw3ZfkYI93IW6/z7UlgrJmtBAwDBpnZusAB7n5usnKFrqiIiIiIxFeUidHMdjazkbNnz8rpdhtzK/WCpZ5VEM12PUmuWFurm8LEY6XC3avCf2cTTEw2DtjS3We5++cxoTpuuYJWWEREREQSKspg7e7PuPvwdu3aN3RVstaYx1dnOxlZsUxiVp8vE1J9OTNvcXWTC9WdWlaotbqe3H2Guz/i7lNyUU5ERERECqsog3UxKZVu4PUN8pkGzUxCcja3CyvGVvR0QnWxymeoFhGRejCL/xARkZLS5MZYS2KZjrlesKQm5XjrbFqpFapzKx+hWoFaRERERGS50miOzZFMW5+zba1uqG7gDbHfRME5m1bqYqVQvZy6fZcGM7vDzN4xs3OTlOluZmMzXU9ERERE6lKLdRyl0v07H7KZKTy25bo+gbrYWqtLOVTnUlMM083LjI4V5RmvV97A3yeZ2R5AubsPNrM7zay3u0+OKdMRuAdoncl6IiIiIhJf002QcbSuKKt3qG7oScsaav+RFurG0koN+Z9FPt8zdOdq+00xVJe4KuCR8OcXCe59Hasa2BeYneF6IiIiIhKHWqxDTbmVOla297fOxX6LRTqhOpvW6tiwG/s8VyE2F6FagbpktQZ+CX+eDgyILRDeugurPUFSyvXMbDgwHKBHjx45q7CIiIhIqWvywboxBuqWzazeIbXQ4bpYQnW6rdSZhup0g24ugnZ9Q7UCdcmbC7QMf25D+j2TUq7n7iOBkQADBw4sjj9aERERkSLQ5IJ164oy5i2pyUugzmUQzdWYZUlfrkN1LlqN891dPJoCdaMxgaAb9zigHzApz+uJiIiINHlNLlhD8bdSp7qFVVrbKKFW62Jorc5lqC5kGM4FBepG50lgrJmtBAwD9jOzS9w91UzfsesNymstRURERBqRJhmsc6k+wTPdAN2yoiyrVutSCtcNqSmGaoXpxsvdZ5tZFbAtcJW7TwE+SlC2Ksl6s/JdVxEREZHGQsE6Q4UI0sUmX+G6sbVUFxuF56bL3WewfIbvvK8nIiIi0tQpWKdQ30CZqzDdkK3WsDwE5yJgF0OghvzfTivfFJxFRERERIqDgjX5ufdzPlqnGzpcQ/1arxs6UBciSEeHXd3yqrQ1LyujU2V5xus1W9y4h06IiBSMxfk89eL4cl5EJFaTCtb5HitczF29GzpcFzJU5zpAZ9sNPNuQXWphunWS8FmKXehFREpCvNAJCp4iIg2k0QfrfIXphgrR2bZaQ+7DdWSb6ZTLlUJ3385VMEwWskspSCcL0anKK2SLSKOm1lURkSYtJ8HazIYB6wBj3H1CLraZrVwH6WJshS6WcA2JW69ztY+GHAedryDYmIN0uttSyBYRERGRxiSt1Ghm3c3sQzNrZmY/mtnr4aNvWGR94EZgSJJt3G1ml4U/X2BmF9S38hAExehHvbZVUVbnIaktWOrLgnT0z/XVGEN1qWhdWZ7TUF3o7YuIiIiIFFK6LdbXAC2BDYAH3f2MmOWvAScD96bYzlFmdlFmVawrV63SpRyc69tqDbnvpt0YAjU07VBd6LAb2V9TPuciIiIiUvpSBmsz2wqYB0wBBgE7mdmWwCfA0e6+1N3HA+PT2N+nwIGZVlJBOj+iz2tDz9gdoVDdMBq69VgBW0RERERKWdKkaWaVwHnAmeFL7wPbuPvGQAWwQ4b7uxk4Ou3KWW7uI53rbt2VNYvr9ciVXB5TLrrS18e8JTUNHqqbomLrkl1s9RERERERSUeqFuszgVvcfaYFs11+7O6LwmXjgd4Z7m8K8CVQBbwer4CZDQeGA6yy6qoZbn65XLdO5yIQLy6rzEFNlsu2K3gyDdWK3Tr8fTV0uG5dWd5kWk2LOcAWc91EGgXNYC0iIpJTqYL1NsBWZnY80B943sxOJujSvRtwWRb7vA74gATB2t1HAiMB+v9lQEb/y+ejq3d9AnWug3S0fITqWLEt2IUI2sUQsBtzt2QF1vS1KDe6Ns/8fDWb33A9P0RERESkYSQN1u6+ReRnM3sd+DvwAGDA0+7+cqY7dPcPzWxMpuvFytd46WIN0hGFCNSJpNNVPFfhu3XU77ehQnZjCtgK1CIlKl7LMqh1WUREpMikfR9rd68Kf9wgmx25+6FxtpWWfE46lqsxz/kO1Q0ZqDORj67kDd2KHS+UlkLYVpgWERERESmMtIN1Qyiz4uveXWilEqjjyXXIbh3zXiiG7uLRiiVsK1CLiIiIiBRWUQfr+ipUgM5Ha3UpB+p48tmSDQ0/6Rko0IqIiIiINFWNJliXUit0Mo0tUMcTCdm5nAyt2EK2iIiIiIg0HSUXrIstQOeqtbopBOpY+bq1V0OPyRYRyYgmKBMRESl5RR2sDS+6IB0tF6G6KQbqePLZiq2AnZ3YMe3R8n1Ok+1bRERERKTYFHWwbsxyGajjhdF0bo1VjJrCWOxil06ojVemPudWQVpERERESpmCdZayba3Od6COXVaqARsUsgslV6G2sYXjymZltGtVkfF65bNK929OpMlTt3wREcmSgnWBtawoy1m4LuXQnKl8h+x4chm8Mwmd+Qj8jS30ikgDiRc8FTpFREQUrLNVWbM461brXIbrpihfk57FaqgwqhBc26+//NzQVRARERERSUrBuoG0jApPpRKyW2YY+ApxXIUK2dIwFs+dxWH77dHQ1RARkWKjbvsiUmQUrOuhPq3W0RoyZGcalnO97Vweb2zXeAXt0hT9e7xlxK2s33cDJn3xRQPWqPSY2R3AusBz7n5JOmXMrBnwbfgAONHdPylIhUVERERKXMkGa1s8L+7rXtm6wDXJrUgYzdk47CLvVhxbPwXtpinRfAFWVsYqq65a4NqUNjPbAyh398FmdqeZ9Xb3yanKAG2BB939jIaot4iIFIBa+kXypmSCdaIgHa9cqYdrKP5AnC/xjjtfk70paMe3cMECZs6cwQorrpS3faQ78V7btm2Z+uefeatHQ0i3ZTidVucEqoBHwp9fBDYDJqdRpiWwk5ltCXwCHO3uSzPYr4iIiEiTVdzpraYGWzwv7VAdkc062aqsWVyQ/TRlLSvKaj1ytt1mFveRT4n2me0jl76ePImLzjmTwf368NfNNmHMqy/nbNuZ1nvp0qU89/RTPPrQgzRv3jxn9SgSGxC0DFeFj3ihelmLMtArbFFOV2vgl/Dn6UD3NMu8D2zj7hsDFcAOceo13MzGm9n4PxvZFx7SyJjFf4iIiORJybRYZ6OxtF5LbfnsPg6pW1NTtXQX8jZoudjXJx99xD/PPYvJX01i/4MO5sXXxvL7lN845MD9+Pdtt7PVNtum3Eb0OalvnWbPmsX+e++Bu/OPk05il1135ZabbqjXNrNR3qIZLbu0yng9+7MMMxsODI96eaS7jwx/HkTqluEqUrc6JzKXoPUZoA3xv0CNV+Zjd18UvjYeqBPmw2MYCTBw4EB1+RAREREJFXeLdQ4UsvVaGka+WrQT7q+Arcj5NGf2bM4983T222s39th7Hz749EvOPu8Ceqy2GhttMogT/3EKRx9+KNXV1Sm3lavjn/TlFwzbpoqNBg7k1ddeY6+996aysv4TBBaau49094FRj5FRi1O2DJNeq3MiEwiCOEA/4Ps0y4wys35mVg7sBnyUwT5FREREmrRG3WIdLRKu89GCnavZwfMhH13Vi/VYI0rxVmaF5O48+/STnHfWGVRttQ1vjHufzp271Cpz/7338O8br+Ou+x6gvLw8r/UZ//57vPbyS7z7zlt88vHHXH7FlRx48MF53WcDS9kyTHqtzok8CYw1s5WAYcB+ZnaJu5+bpMwg4GPgAcCAp909d2MBRPJBkzCJiEgRKcpgbWY7AzuvsXrP3G87T93D4wXYQgbQQo71jt5XKYVsUNBesGABxxxxGN999w0j7ribTQYPqbW8pqaGSy/8J88+/RRPjf4fa/Zeq842Fi1axOeffcrEDyYw8YMPmDTpS/bedz/+dtgRVFRUpF2XKb/9xnlnn8FHH0xg9z325O//OInBQ4bQrl27eh9nkRtlZpcCnxK0DF8Wp0ykRXkcQYvypHQ37u6zzawK2Ba4yt2nENP6HKfMLGAWwfhvaeriBdb6htXGEoLzcW5ERKRRKMpg7e7PAM9s2L/fUfnYfnTX8HyOwc427MaG1WKeIK2UQjY07aC9ePFijvjbgbRr355X3ng7bgh+a+wb3HvXnYz78KNardjTpk3lxmv/xbi332LSl1+weq816D9gQzbceGN232tvbr7xeu68fQTbDduRddfvyxZDq+jWPXnv5ROPHc466/Th9gkf0LJly6RlG5mLiGoZBn5Ns0U5be4+g+VjtLMuIyIiIiLpKcpgXUiFCtmZKOYgnUyphWzI7+29Mt1vOrKt29KlSzn2yMOorKzkpltHJmxZHrzpZqy40op8MH482263Pe7O4488zAXnnc0uu+3BxZdfyfob9KNVq9qTeg3dciveeetN3nt3HE/993FG3HwTL415M2F9WlaUMeW3X7n66qubWqjG3T+lbsvwuTFl4rUoi4iIiEiRavLBOlo+x2E3NQ3dNb4+ctGqna9J1NKp26JFi7jwvHP47JOPmTdvHnPnzmXWzBn0+8sA7nng4aTdtce/9y7Tpk3j119+5rGHH+LhB+9n2tSpjHroUf4yYMOE65kZQzbbnCGbbc6zTz3JrTfflLTu7777Ll9NmsQvv/xCn3XWwZLcBqdUv2iqL7Uoi4iIiJQOBes4YmcRV9DOjVwHpEIF9ULMNJ6t2Lr9OX0Whx60P+3ateOMc86jdZs2tGnThtat29Cte3fKyhIfy+hnn+Howw9h6dKlXHHJRQzedDN22X0P9jvgoLTHTrs7N11/Lf849f8S1hGgb9++XHLpZZx6ysn079+ff99yK23btq1VpqkGahEREREpPQrWaajv7boUzPMjWfAqldbxXPvb/nuz+uqrc9PNt9SazTudVve5c+Zw7gUXs9nmW7DOeuslDeGJzJs7l08/+ZiqLTZL+oVEq1at+PtJJzH8mGM4/bRTqdpic+5/4EH6rLMOoFAtIiIiIqVFwboAsgnmCuP1U8pd0etjwIAN+eKLz+t0rU6n1f2Qvx1Ur33X1NSw398OpG/fvixdsiStdVq0aMGN/76ZUffcw8477sDYt96mR/fO9apHrjRrXk7Lji0yXq+svHTuZS5S9BrLbOIiItLoFW8f1ybOFs8r2KOpqKxZXOfR2Fxw0UUsWLCAKy6Pdwen/Lr7rjuZN38er70xlu4rrJDRugcfcghHHHYIxxx1BDU1TWemdmlkzOI/RBoDvb9FRJJSsJYmG7Sh8YXtiooK7r7nXu69+24O/dvB3Hj99bwxZgxz5szJ+75vveUW+vTpw8yZMwH48ccfmT17dlrrVtYs5uwzTmfRokX867ob8lhLEckbhS4REWnCFKyljqYctCF+2M40dCfbRrqPbHVfYQVeHTOGv263PT/9/BPnn3cu22y5ZcLyb4wZw6aDNuGgA/bnnbffxrPsYvnEU0+zZMlSNlhvXW65+Wa2rhrK+uv04bhjjuann35KuF7kWJs1a8bdd9zOTTffwrvvvZ9VHUSaFLUgijRu+vsWKSkaY11PZQvr1xJY06Jt6kINzBbP05jvUCFbtOszTnyllVbmgAMP5IADD2T69On077t+nTLTp0/nvHPO5tVXXuHyK6/ijz9+57hjj6Fd27YMP/oYeq2xBi1btmTllVema7duKfe5yiqrMOL22xk54jYeuP9+zj3/fIYN24Ebb7iBffbck5dfe43WrZe/j+Id36qrrMLNN17PIUccybixY+jQoUNaxysiIiIi0pAUrOuhvqE6sg2Fa0lXZc3ijCdh69ChA127dWPoZpuy/4EHsffeezNmzBjOPP3/2HnXXXl3/ATatWsHwJFHDed//3uB++69lz///JMF8+fzyy+/8uXkyVRWprff4Ucfw/Cjj1n2/OJLL2Xq1Kkcf+wx3HXPvTT35BOb7bzTjvzvpZe5/MqrufLySzM6VhEJadIvERGRglJX8HrIRSAuhVANmqW8mGTaVbysrIz3xk/g/Asu5P333qPveuty5eWXM+qBB/nXtdctC9WRssOG7cD9Dz7Eiy+/wti336Hn6j156803s66vmXH9jTfy4/ffcdO/rk5rnZP/8XceeOhhFi8u7THvIiIiIjmlYUBFSy3W9VTTom3WLdcK1bmTaix4KRxDpjJpvS4vL2frbbZh6222Yf78+VRWVtKsWfw//0WLFvH555/x0cSJTPzwQ3795Rfefvstttxqq6zq6Qvm8PnEjxgyaBDnXXAh2/11G/quX7drerQ1eq1Onz5rM/r5F9ht112y2q+IiIiISKE0+mCdLPTmKtiWSkBujNKdXC1ZuVIO3dl0DW/VqlXc1++/7z5uu+VmvvrqK3r1WoN+/fvTr39/9tv/APr/5S9Z1Q1gq513448//mCbrbfizttHsPZaa6W1/g7bb8/Yt95SsBZp7NRtXXJN7ykRaQAlH6zrM8451bpNPTAXe+DM1Yzlsdsp9uOOlU24jvX86Oe48J/nc+fd9zBgww0Thu906hJr662q+PXX37g+zW7gEc2bV1JdXdNgM9OXV5bTomOLjNcrK9cIGxFpguKFWQVZEWlCSvoKMBeTh6Xafr73UayKOVzm+zZgpXi7sfrcouudt9/m+GOP44GHH2azzTdPO1Sne4uwIw47lMefeIIZM2ZmVK+ysjJ8qcZYSwPQ2DUpdRqDKSJScCXZYl3osFsqM3fnQjEHashdK3Wh91mo85pJ6/UXn3/OZZdewrvjxnHriBEMHLhR0u1ma4Xu3dl+u+249777+MeJJ6S1ji2eR1nNEmpqarLer4iISB3qJi4ieZJWi7WZdTKzbc2sS5Iyw8zsFDPbMHfVq6uhWpCbQst1MYfqUmo9jie2FTyfLeKpWpC/++47jjz8cHbaYQc22mhjJn7yKdttv33CbeTi3t3HDj+K227/D57GhUvknJgZNa5gLSKNQKm0HqulW0QkaymDtZl1BJ4FNgZeM7OuZvajmb0ePvqGRdcHbgSGJNnW3WZ2WfjzBWZ2QUaVbeBw29D7z6diDdWlHqjTkc9jjA3HU377jb9uvRVr9l6TiZ98wt9POmlZ1+9cBulYK6+8EnNmp/77iZwHd2f0iy/Tq2fPnNdFRERERCTX0ukKvgFwiruPC0P24cCD7n5GTLnXgJOBe1Ns7ygzuyiTShZToG1s3cKLNVA3RctCZZ5+J+VLFjD8iMM48vBDOe+M01hcVpmXEB3P2DffYrNNh2BJWj6iv1y4+/6H+PW3Kfz9mCMLUT0RERERkXpJGazdfQyAmW1B0Gr9GLCTmW0JfAIc7e5L3X08MD6NfX4KHJhuBYspVDc2xR6qG3tLdSL5CtjXXHs9S5cu5azT/w+o37jpTI198y0232zThMun/fYT7034gPEfTOT9Dyby/oQPeeXZx6msrN9s5yIiIiIihZDuGGsD9gVmAB8C27j7xkAFsEOG+7wZODrJvoab2XgzGz916tQMN10YjSHsN7VQHZnhPfpR7HJ9Dp557jmWLq3m/fETcrrddPz662+ssvLKtV6zxfP45bvJHH/C8ay30WbcdNt/WLp0KcMP+xsfjxvDeuv0KXg9pcRoPKg0dnqPi4iUjLRmBfdgxqHjzexiYCV3HxsuGg/0znCfU4AvgSrg9Tj7GgmMBNiw3/ppTdFoizIPIN68uINlvtnieUUfrjOVaVguhW79ufw9vfbS/xh1/4P87fAjWKdPH84/52wGbjggJ9tOpV+/Dfjok0/YZuutmPLTd/z+x5888cxz3PfQYxx+8AF8Pv5NOnfqVJC6iIiIiIjkWspgbWZnAL+5+71AB+A2M/uSoEv3bsBlWez3OuAD4gTrTGUTqiPrKVwXZ7jOpqU22xboUgjXuVJRUcHhh/6NA/ffl7vvHcW+BxzEfvvszaUXX5j3fQ/eZGN233tfrrvhRrp368aK3bsxcEB/PnzrVVZcoXve95+NZi2a0apz5n8fZc3S6ggkItIwdLspEZG8SKfFeiTwiJkdSRCmtwDuBwx42t1fznSn7v6hmY3JdL1ci4TybAJ2YwlkxRquC6nYf5e5/h01b96co486kj133531+v+FU08+iU6dOuZs+/FsXzWEaT98RatWLZNOYCa5YWZ3AOsCz7n7JemWSWc9EREREakrncnLZgDbxry8QTY7c/dDo36uymYb0bJtrY63nabeel0sCtlaHbuNYg/XkNux8V26dGb77bbjgYce4oTjjs3ZdqMt+32a0bp1q7zsQ2ozsz2AcncfbGZ3mllvd5+cqgzQN9V6IiKSIfUQEGky0hpjXYxyFapjt9cUA7ZarQPFHq4h9wH7yMMO5e8nn8rxxx6T05bkpjqje5GoAh4Jf34R2AyIDcjxyvwljfWWmTRpElVVVbVe22effTjuuOOYP38+O+xQd17LQw89lEMPPZSpU6ey11571Vl+7LHHsu+++/LTTz9x8MEH11l+6qmnsvPOOzNp0iSOPrruHJjnAtsAEydO5KSTTqqz/LLLLmPIkCG8/fbbnH322XWWX3/99fTv35+XgTrN9VVVjBgxgrXXXptnnnmGf/3rX3XWHwWsCjwM3BqzLsBjjz1Gly5duPvuu7n77rvrrD8aaAXcQviLiDm/r4f/XgM8G7OsJfB8+PPFF1/MKzHb7gw8Hv581lln8U7M8lWA+8KfTwImxixfa/hwRo4cCcBw4KvohVVV9O/fn+vDpwcBP0ctAxgMXB6+tCcwLWb51sB54UvDgAVRywB2Ak6LrBKzDGAf4DhgPrBDzDKAQ8PH1KlTqfvOg2MJZmj9Caj7zoNTn3kmeO8RZ/bVqirOPffc4L1HcP6il0EwZm4I8DZwdswygOuB/lD7vRe1fASwNvAM8K+YZRDz3otz/I8BXSB478U5vjrvvRivh/9eAzwbs6zlsGE8/3zw7rsYar/3qqro3Lkzjz8evPvOOuss3nmn9rtvlVVW4b77gnffSSedxMSJE2stX4tw4h3C917M8fWH2u+9mOW13nt77rn8vReq9d4bNowFCxbUWr7TTjtx2mmnhYdTe9uQxude+JgKtd974baOHTOm7ntv6NBlxU4Fdobl772YOpx77rlss8022X/uEb73Xn6ZSy6p21Ep5efeqFGsuuqqPPzww9x66611lqf83Bs9mlatWnHLLbfwyCN1332vv/46ANdccw3PPlv73deyZcvl772LL+aVV2p/8tX7vbfWWss/94YP56uvan3yBZ97118PwEEHHcTPP/9ca/ngwYO5/PLg3bfnnnsybVrtd9/WW2/NeecF776s3ntEfe4FhWotz/v/ufV970X+zy3B914iJRmscx2qY7edbrguhSCWrmII1w3VWh27vVL4nebq97XZpkOorq5m7FtvscVmm+WkXrky9efvefCJZ3li9Is522YT0Rr4Jfx5OhBvhrp4ZVKuZ2bDCa5tad68ee5qnMSYMfF/HjqUWhefAJx7LmyzDYQXR9HlATbddHkjUeyyWl56CeL8Jw+JG5+GDgV+/BFWXRUefhhi/pMfMwa6dk2wXuQ4Ro+GVq3gllsg8p989DGG/8lzzTXw7LO1l7VsCeEFJhdfXPfcdO4Mjz++/Hns8lVWWf7zP/6x7BzW4Q7Dh0PMBeayZQAHHQQxF5iceSaEF5jsuSdEX2AOHQpbbw3hBSbDhkHkAjNSz512gvACc9nFY/Qx7LMPHHcczJ8PO+xQ9/gOPTR4TJ1adxnAsWGvnR9/hDgXmMt8+SXEucAEguOfOBHiXGDy1lswZAi8/TbEXmAOHQrXXw/9+8PLL9d+70XqOmIErL02PPMMRC4wo49j1Kja773YY3zsMejSBe6+O/7xjx4d/Hvzzcvfe/GOL/LeS7T84ovhldivdeL/3USqcf/9wSPeMoYOhbXWgjDc1HrvRQr17x+cP1j+3os+xsGDa7/3Yo8/6r33wgt16zlmTPDWS/S3v88+KT4X7rpr+XsvTrjhoYdg333hp5/qvveGDoVTT4Wdd4ZJk2q99yKfYXU+F2Ne23TTmOWxxx/13ov3udinT/Cr3WWX+McIyY8/3udeZNmYMdA6zqVM9HHEbjv2GKOXxzv+yPIzz6y7DIL3XqL/F8aMgdtvD5bffnvdeo4ZE5y+RMc/eHCK90YSY8YEj//7vzjrRVaO/dyLWjfy72GH1d5f7P+p++0XfOzFLos8T/Z/ZmT5X/6S+DiSHX+i9SLvjT5xbg4TWQbQo0fdZQ3NvIi7omzYb31/9/nHa72Wz1AdK52AXQohLBMNGa6LIVhHlNLvtb6/swcffoRrr7+Bt8a8Vq/7RucqVEd+p2ddeg1PjH6Ray88m10POWaCuw/MyQ7SYGYb7rNOz/GXDN0w43V3f+wVPv1zRoMNJDezG4AH3X1c2OW7j7tflqoM0D3VetEGDhzo48ePz+ORROoa//V0/uuKt25kvWTL8lEf9QaVpiwff4v5kOzvNNtl+ZLpOU2nPvk4/vqct2zfN7lclk5d8/HeaIj/+4rp3BTb31s0M0t4Tarpa5MoZIgvFg3VhbeYQnW+t51rtnhevX5v++2zN6uusgpXXlO3m00h9h8Re3/xfxx1CDNnzaZXzx5J1pI4JhB04wboB3yfZpl01hMRERGROEqqK3jKoLtgdvoba9ku7X0ma7kula7DkrlS+91m2z3czPj3jdezyZDN2WWnHem3QfpzE9Y3UCf7AmOFbl058+/HcPJ5mpw6Q08CY81sJYLhqvuZ2SXufm6SMoMAj/OaiIhI1tQrR5qSkmmxThqqF8zOLFRnuE6qQF9KrZvpyFULZCb7y1Shznmp/W4jv7tMz+lKK67IZZdcxFHHHFdn8oxk+8pWbOt0IscfdiA1NfpfORPuPptgfqdxwJbu/lFMqI5XZla81wpZbxEREZFSVhIt1nntkh0J1ylasFPNGh4JCaXUwplKPsJ1pEW1vqGskEqt5Toi0xbsgw7Yn1dee51DjziKB0bdQ3l5edJtZyuT319FRQUvPHQnlausk/X+slVeWUnzDpn/3i3JeSuU8DaJCWYgSlwmnfVEREREpK6SabFOKNOW6mTbSWNbtmhe0qCfbktcU1Xf1vCGOrel+jvN5HybGSNuvomZs2Zx2hlnkmhiw0L//nJ5GzARESku7nUfIiKlqOiDdcEnEIsE7BQhWwG78Br6fDb0/usj3YDdvHlzHnngPsa++RbX3XBT3O1kq5TPn4iINH7xQr6Cvoikq+iDdVK5aq1Otv0cBWyFivoplvNXLPXIVjoBu3379jw46l6uufa6jNZLptTPm0h96YJdmjK990WkKSjqMdZWU9PQVQikMQ471RhsaJzjsAuh2EJZqY65jpYyXLdsRnl5Wc5uo5WtpnjLOxERkVT05YRI8SnqYF10oluvE4RsBezcyiSUZRrCkv2OUmkM4TqZJUuWUFlRUe/tKFRLQ9KFp4iIlCL9/1WaSjdY57sbeLr7b8IBO93QlO7xFTqEpbpHeSqNMVzX1NTwv1de44ZbRtJj1VXqta1sf59NIVCb2R3AusBz7h73Rt3plBEREZHipYDctJRusC4WKVqx0wlvpRKwsw1K+e7KXZ8gpnBd276HHMU3333PKSccw96775LVNhSokzOzPYBydx9sZneaWW93n5xpGREREcmMgq7kU5MO1tWzpqVdtrx959SFErRip9N6DcUV0optXHMiuQhjCtfL9eu7Hl26dOag/fbOan2F6rRUsfxe0S8CmwGxoTmdMlIPurgSkcZKn28iDaM0g3WW3cAzCdLJ1k0ZshfMTth6DcXbPbxUwnQ+KFwHjj78ENbfeHMOOWAfBm00MKN1s7pHdREH6vIWFbTonHjCwkTKmpVhZsOB4VEvj3T3keHPrYFfwp+nAwPibCadMiIiUiAKqyKSSukF6wYI1cm2lTBkJxmDnUnAjsh1aCv1EJ2PQJaLcA3F36U/ma5dOvPvf13OgYcfw1/6bcBlF5zDWmuukXK9TN9PxRyocyEM0SMTLJ4LtAx/bkP82x6mU0ZEREREikRRXqyZ2c5mNnLmnJiL9SII1fG2Hf2oI3Iv7Dh1j9wDO9W9sKH2/bBz8cilTI4jV/vK5/brK9/nO9/23n1XPnv/TVZcoTsXXn51wnLZHF8h3iMlYAJB126AfsD3WZYRERERkSJRlC3W7v4M8MzAvusetezFIgzVqfZXpzU7jYnOIurTcpov6QaiROXqc0yFDGPpjonPRLzwmW3LdjpBtr6t5i1atKBrl8506tghq/3HUpiu5UlgrJmtBAwD9jOzS9z93CRlBhW8liIiIiKStqIM1rXU47Za9QnVNXNm1nmtrG2HrPYft7t4BiE7Vr5CdyFam0tJfbuGp5LPluxcDCX46JPPOGjfvRJuMx2l9jsvBHefbWZVwLbAVe4+BfgoRZlZBa6miIiIiGSguIO1V2e1Wq4DdTrLIHHwTjkmO0XIjqWwEqM+9zRPcb7z0XrdEFK1mMcud3cmfvQxV597asMG6oa+X32euPsMls/6nXUZERGRpkiTyUkxKu5gnYVsQ3Wq0JzpNnISsiGtoN1k5SJ0JZjBPVa+W68bQrLA/MkXX9GsWTlr9OyR0TZzEqgbaZgWERERkcarUQXrbEJ1LgJ1OtuNF7Qzml08VqEDd7ZhJx/1zHXwSjKDe7TGGK4TefL5F9lt2LaYWVrl6x2oFaZFREREpIQ1mmBdTKE61b5ShWxI817ZqaQKtYUIM7lsgc93fdNovW4sXcMTqa6u5rsff+axZ17g1qsvTlm+MQfq8uYVNO+Y+dh0Ky/PQ22kWKj7oYiIiMRT8sG6UIG6JsP9lCUJxjnpMp6OYgwtxVinaE24a/gNt9/DeVdcR7cunRi80QAGb9g/brnGHKZFRERERLJRssE634E60yCdzvrxwnamXcYjsg7bjVQ274ek3e8zmESuMQTsjz+fxBU33sbHrz9Lz1VXiVumXoFaYVpEREREGrGSC9b5npysvoE6k22nCtrJbu9V6LCdyXkvZOivzwzw1bOmpR7b3gRar5csWcLhJ53JZeecVitUayIyEREREZH0lESwrk94gvRCdT4DdTr7zLY1O1p9z1OuZDxevJ7br++2ktavRFqvU4XgRPWyRfO4/PoRrNS1E4fttp3CtIiIiBSE5qyQxqaog7VXL83bPalrlctyH0tmJw4QFe0ym6QrUod0x2ZHpArbxSAXQTufXxpEtl3fruFQN+DmOmhnG3wTrffBp19w2/2PMuHZB9OeATwuhWkRERERacKKOlhnK5+BOlmYTlYu3aCdqhW7TvkUx1qMwbtYWtZj5apreLREgTY2cOekpThDS5cuZfiZF3PVWSexUveumW9AYVpERKTJU8uzSKBRBet8Bep0w3Qm20gnaKczJjvlNnJ8S7FiDOqQebf5RNLqGg71vj93QwTpWDff+wgd27fjoN13zGzFAgbqYv0SpliZ2R3AusBz7n5JuuXMrBnwbfgAONHdP8l7hUVEREQaiZIP1vmc6TsXgTrdbWcTtKNlE7qzke75LkQAT1aXRMvSqVfKcA0ZdQ8vRj//9juX3fwf3nj0zvS6gOc5TCtA15+Z7QGUu/tgM7vTzHq7++R0ygFtgQfd/YxC11tEpNSpxVZEoASDdb7vQZ3PMJ3JfrMdo51MocI35K4FOdV267N+qlnX0wrXUJIB+5SL/8WxB+3D2r161l5QgNboUgnRZRWVGf8dAlh5eR5qk5Yq4JHw5xeBzYA6wTpBuZbATma2JfAJcLS7L81nZdOlC1YREREpBSURrLMNU6UQqBNJVJ9sLvQjGjJ8ZxO0c92NPdH2E9UlrXANpRWwF8xm9Bvj+OizL7j3stPVEl3CzGwEsHbUS0OBO8KfpwMDEqzaGvglptwrwDbu/puZ3QvsADwdZ5/DgeEAPXr0qO8hiIiIFA19kZt7Te2c5ixYm9kwYB1gjLtPyMlGq6vzHqqLLVCnkk598xW+cxm68x2aM1EzZ2b9wzU0XPfwDMLx3HkL+MelN/Lv806iRfPKnFZDIbqw3P3o6OdmdgNByzNAG6Aswapz45T72N0Xha+NB3on2OdIYCTAwIEDm9h/lyIiIiKJpQzWZtYeeAgoB+YB+wJfU3eSm/WB64BjgbjB2szuBn5197PN7AIAd7+gXkcQo1gC9aIZczIq37xj25ztO9WxZRu8E53bQnYxT6Zm1rSs65IqXEOatwmLF3JzHbazbGV2d4b/82q23OQvbLfZxvWuhoJ00ZlA0K17HNAPmJRBuVFmdinwKbAbcFm+KysiIiIST6m2dKfTYn0gcK27v2RmtwJnEn+Sm9eAk4F7U2zvKDO7KPOqJtcQgTrT8JzLbdUniOe6m3m8c5/vsJ3o912fmdSThWvIsPU6WiZhO49ds28c9TiTv/+ZN+67KWGZxYuXcPnt97PzlkMYsO5adZYrTBe1J4GxZrYSMAwYZGbrAge4+7nJygEfAw8ABjzt7i8XsuIiItLwSjXMSNNSzO/TlMHa3W+JetoV+Ik4k9y4+3iCLoSpfEoQ1uut0GOocxmk6yvdumQSwOOdo2IJ29ncczx2vXTvC54qXEOardfJFPge0G9O+IQr//MAbz1wMy1bNI9fpYWL2PeUC5gzbz4jHn6aw3faiguP3r+g9ZTsuftsM6sCtgWucvdZwCzg3DTLbVDI+oqIiEhhFXMobQzSHmNtZoOBjsBLwF2pJrlJ4mbgdGB0gv0smxxn1W6d6iwv5C2ziilIZytyDNm2cEefv/qM3Ybsw3GupNtVPFW4hhwG7AL47c9pHPh/F3HHpWew+iorxi0z89ef2fOMK1ixS0ceufhsfvlzOuvtcyJnHLIHrRIEcSk+7j6D5TN+17uciIiIiKQnrWBtZp2Am4A9gSnpTHKTxBTgS4JbvrweuzB6cpwN1+rpkF0gyzZQN4YwHc+iGXPqPY47lyG7odRnHHY8xR6w581fwP6nXsThe+7IsM03Aep25545Zx47nXwxG/RenZtOO5Ly8nIm/fALm6zfW6FaRERERCQNiWaNXcbMKoFHgbPc/QeCSW76mVk5wSQ3H2Wx3+sIbg2TlFcvzXuoXjRjTq1HY5bL41sye3atR2OT6azl1bOmFd344zFjxvKX3Q5nzRW7cPYBOyas49m33Md6vXpw8+nDKQ/vwfzs2PEMG7xhoassIiIiIvXgXvchhZFOi/URBPc5PcfMziGYpGwU9Zjkxt0/NLMxma6XSroBr7EH6GTq2zU8kVyOz863XHYJjxUdXAvZih293/kLF3H+iAd4/JV3uPH/jmLnzTdKuN74z7/muTff56MHbsDMAFi8ZAlPvj6O10dcmvd6Q3Hdei2aVbbIqneDhV9OiIiIiEjTkc7kZbcCt8a8fGE2O3P3Q6N+rspmG/EoUGcuXwE7WrLfS7GG7lxK1oJdn9CdbLtvf/wlR116MwPXWYPxo/5F5/aJf7/V1dX8/ZrbufS4g+jQtvWy159/+0PW6rESa6yyQlr1KdZgLCIiIiJSKGlPXlaMSi1QL5yWXZfpFp3zF0ILEbDjyab7eEOE8WxardOR627j8xYs5KL/PMxDL47lhlOPZLeqQSnXueuZV2leWcGB29celTFq9GscvGNVnfIK0CIiIlLs1PVZGkpJButSCdTZBulE22mMATsTub4dWLrdfPMVrnPB3Xn6jfc47fq7GLxBH8bf+y+6dmyf1rp3Pv0yFx69/7Iu4AB/TJ/FGx98yh2nHKIgLSIiIpKCgrxElFSwznRSskLLVZBOZ/v5CtnR562YQ3ZE7HsiX63a0SGzWEL2Nz9P4eTr7uD7X/9g5DnHs+XAvhmtv3Lndsz4889ax/bgMy+x46D+tG3VIse1FRERERFpvIo+WGfaZThfgTrfoTlThQ7Z2WqILubphutsb70V25Jb6KC9YNEirhn1JLc+/gKnHLgrj11xOpUVFSnXi633WquswOSfp9R67b6X3ubKo/fJZXVFRERERBq9og7WXl2ddtlsQmCxheVsZdtV/JynxvD6pB9pVdmMVpUVtGvZnPN33JTe3TrlrG6Z/F5yFcIzCde5kM+gPe6TSUz64Rd+mzqDX6dO57epM/hw0rcMXGdN3r37albt3iXtesVat+fK3P/S28uej5/0HTPmzmOLDdbOVfVFRERERJqEog7W6cgkuDWWIJ3IwmmzMwrX1dU1bN1nNfb4y9rMX7yEd779haPve4Hn/74PzZsV/q2R6HdZCl3So2UyNjk6hH/x3U9cfvfjXHniIbRr3ZKTr72DMR9+xub912XFLh3p03MVthrYl/OO2IcNeves134B9thsQy6//xmefvtDdhnyFy4Z9RSn7L09ZWUpb28vIiIiTYjGEYukVtLBWqG6flo3r2RJdTX9V+0OwOBeK/PZr1O5dPTbXLTLFg1cu+UWzZiTcbhOt9U62+7guRIJw2989CUHXzaCLTfqx1bHnkez8jIGrb82E+79F21atUy6braaV1bw778fzBFX30nzimZ8+eMUHvnnCfXapoiIiEguKMxLqSnpYJ2OphSo47VWz1+8hMuef4e1unVk6No9+OaPGcxcsIiNe67E/e99xov/2G9ZWTPjX3tvzaZXjeLQwRvQq2uHAtY+uWzCdSn48Y9p3P/S29z61Kvce/Zwqvqvw30vvUXzigr2rtqYsqhQnY9Zurfo14ct+/dhnwtv5t9/P5jKikb/kZC+isrsuvWX6xyKiIiINDUlewWYTmt1Uw3VsxcsYv7iJTQrL2P0J9/w7ne/MGvBQq743zssqa6hzwqdOemRlxm+eX96dKodxju2asE+G/bh/vc/47wdNi30YSSVabjOV6v1r9Nm0q1DW5qVl6e9TrQZc+bxxNgJPPjqOD7//hf22HwgL11zOmv3WBGAg7Zdft4Lccury47am9YtW7D/1qnvfS0iIiIiInWVZLBOFaqbUqD+8o8ZfDZnLpPemsaXU6Yx6ffpzF64mDbNK1haXYMDIw7cnqFr9WDR0qXMWrCYbm1b8fHPf9C7e/xJyvbesA9HjBpddMEaGq7letHiJdz2zGs8+Mo7/PD7NKpratho7dXZdP212HT93mzUZ3VatWheZz1357fps/jml9+Z/PPvvDj+U1778Au23nBdTtxjW7YbuD7NK1PP6J1PXdq35brjD1j2vGbWtLztqyG73YuIiIhI5tQtPz1FHaxnzFtY57XYUO3uTPhxCi9/8T2nbrsx1TPnFap6Da5F53bsc8ODVK3Vgw1W7sbmvVdl7e6dWaVDW8rKrE755s2a0a1t8CvfYJVuCbfbq2tHfp05l5oaj7udZLL5UiPT2cwj74F0AnYuWq3f/mwyx113D71XXoGrjt6XTddfi1nz5vPO51/z1qeTOf/u//Lptz+z0+D+3HPWcACmzZ7LHuffyGff/ULrFpWssXJ31lypG9tv3JfbTjmUDm1aZXDE+ZHPAF1M+xQRERERybeiDta/zpjNHa9N4IgtN6yzbN6ixTz+4Vfc887HzFm4mHkLF7PVKt3ZYMXEtx8CWDQzwczTHUpr/G4kjLZrUck5OwxhtU7t0163ece2SVv9W1Y0o33L5nwzdUbat96qTy+BbG8Xlm7rdba335o9bwHn3/Vfnn77Q649bn9222z5+7BTuzbsOKg/Ow7qD8DBl41gtahbXz355gS6dWjHMw+cTLvW8ScfaygKtyIiIiIiuVXUwXrNFTpz4//G8dnPf7DZ2quxRttW1Lgz6t1P+e8HkxjUa2XO3WFTNuncgdNHv8VHv01NGKwTBep0l2ci3yE9OoB2bNWC6fMWphWso0NoqnB9xKb9uOrFd7n9oGEpt5urrvfZBOx8dQ1/9p2JnPTv+9luo758MPKipC3MCxYt5omxE/jx4WuXvfbfN8Zz5I5VRROqFaZFRERERPKnqIN182blPHHy/tzx+gc89vbHTPp9GgsWL2XfgevwyskHsErHtsvCWL8Vu/DxlPjhIZehOR35bBWPDp1zFi5ixvxFLK2uSbpOouAZeT1ewB6+eX+GXHUvb3/zM0PWWCXhtvMxnj3TgJ1OuE6n1bpm1jRo25ETbhjFGx9P4o7/O4Kh/fuk3H/L5pVs1nct3vh4ErtttiFTZ81h/KTvefSC9dOqfz4pUBc/M2sGfBs+AE50908SlL0DWBd4zt0vKVAVRURERCSFog7WACt0aMtpQwfA0LrLokNdvxW7cN+Hk+qUKXSoTia6LtmE7OiguWjpUg695zm27rMaA1dbIW75dFty47Vet6qs4Nq9tmb4fS/w9HF7NcittzIJ2LlquT7vzv/yxY+/8t6t/4w7GVkiB2w9iPtffodN11+Le198k60GrJvR+rmmQF1SNgAedPczkhUysz2AcncfbGZ3mllvd59cmCqKiIiISDJlDV2BVNK5rRZA9zatmL5g+WRni2bOKapQHSvTukWHy1kLFnH8gy/SoWVzLtttKGa1Jxhr3rFtxiEz3jpb9enJ6dsN4oA7n2LavAV11inU7Ovp7ifVe2XJ7OTbGfHC2zzz5ngeu/DEjEPxbpttyNiPJ7HBEedw9wtvsuumf2HugrqT7+VbzaxpCtWlZxCwk5m9Z2Z3hC3Y8VQBj4Q/vwhsVojKiYiIiEhqRd9inUhs2HKWzwNfzIE6G5FQPWXWXEaOncgD73/O9uv14ordqygvW/7dSC5abGNbr/82aH1+nD6Lv931DI8ctRutm1cC9QvVkd9PJq32C6fNzmvL9TPvfcZVT7zKKxcfR+d2bdJe74+Zs3njo0m88dGXdO3Qlumz51FRXs4Fdz/J6bc9zPsjLqB7x/QnlsuUQnT+lFU2pzyL24NZeTlmNhwYHvXySHcfCWBmI4C1o5a9Bmzj7r+Z2b3ADsDTcTbdGvgl/Hk6MCDjyomIiIhIXhRlsDaznYGdV+/aMe113MHI7NZQpaBF53Z8++dMbh4zgWc//pq9NuzDSyftx6oda4fMXE7gFRuuz95+CKc9/irrXfgf1u7eie3XX4Nj+6+dZAuJRX/psWjmnLyE62TijbX+4uffOWHk4zxx1uH07NYp6a23ItydC+5+gtuefo3N+vZmi359OGLHofRdfRXKwi87zhz5CBfe/SS3nHxIveocS2G6+IUhemSCZUdHPzez5u6+KHw6HuidYLNzgchseG0ogR5HIiIiIk1FUQZrd38GeOYvPVc8Ku11ACuhXJ1OoJxVUc4Jo0bz9jc/c9iQDXj7jL/ROc4s0/mYFTs6XJeVGdfuvTWX7LoFI8dO5LVJP2QdrGPlI1ynarWODdf3vz6Bw7bemAFRk7QlC9fV1TWceOMoPv72Jz67+zK6tK+7r3kLFzHx6x9Ze9X4498zpTDdqI0ys0uBT4HdgMsSlJtA0P17HNAPqDuphIiIiEgJcE9dptQUZbDOlpG6G/j8afNqPW/VuXUeaxRfOkFyXosK9r71cbZdZ3Vu2GebZV2w62wrD6E6kVaVFSxYsjTte1vHSvS7achw7e48/d6n3HPSAXXKJArXD7zyDh9/+xPPX3kabVu1qLO8urqGvf55E6t268S1x9Xdbrz9SJN2EfAAwUfY0+7+spmtCxzg7udGlXsSGGtmKwHDCMZmi4iIiEgRaDRdCds2r2DWwsUsSXLrqdhQHXkt3uv5kk6AXNyqOfvd/iTbrdeL83bctMFCdbztHz5kA57/7Bu+mF93MrNCSmeMdzqTmX0zZRoz5y2g/+orp73vRUuW0m+NHnFDNcCL4z9l9rwF3HbyoZSX1/0Ti0wwponGBMDdP3X3Ddy9r7ufE772eUyoxt1nE0xgNg7Y0t1nxW4rnPzsHTM7N3ZZTLnuZjY2m3VFREREpK6iDtaeICTHC1Vtm1fSo0NbPp86M6t9RQJ2PkN2OqF6/uIlHHDH02zUcyXO3n5w4m0VsKU6Wvd2rbl016H8/aEXWbhkadrrpepJkK8J51KF65U7t6dbh7bcP+aDuMvjBd8Wlc1YuHhJwm3eMXoMR+1UlTBUN7Qls2en9ZDi4+4z3P0Rd58Suyz6dlxALzOLO1bbzDoC9xBMhpbRuiIiIiISX6PqCj5wlW58MGUa/brX7aqcSWCOLpurruLphOqFS5cy/Ok3WKNbRy7ZZYs6t9Fatq0GCtURu/Vfi+c+/YZrxn7IuVttlLPt5msys2TdwpstXMA9/ziAHS4cySZr9aD3Sl3rlIntEt6ispKZc+fh7rV+R3/MnM1l9z3DhK++556zhsfdTkNKFJZrapwxX3zHoqXVmEG/HiuwQoe2LJk9mzkLF9G8WTMqm5Un3XbshHCZ7F9ypoq6t+OKd5/ramBf4KlM142e7bxHjx45qLKIiIiUssY4VjpbRd1inamNVunOhClTc7rNXLRgpxMWFy2t5sTRb9GpdQuu3WsrysqKM1RHXLF7FU99/i3v/lin4ayOTFqjM225zsW9tNfq0Ipz9tmWQ294kEUJWuGju21vuFZPvv7lD/ofeR5XPzyab3/9g6sfHs2Ao86nWXk540dcSOuo+2A3ZJfvdFqg//v+55x4z3Pc+foERrwynkHnj+Tv9zzHsx9OYuNzb+O659/OaD9qAS8MMxthZq9HHsCJ1L4dV/d467n77DjdyGNv5ZVo3ZHuPtDdB3btWvdLKBEREZGmqlG1WPdr15qLpkyr05JYyDHUsdIJ1VPnLeCYp99gxQ5tuHn/v9a6N3WtbRVJqAbo3LolF2yzCZe9Np6nDtkpp9tuiMnMDh20Lq989BUXPPgCl/8t8fHUzJrGaq3KmPifi3n3i28Z9eJbbHTMBWw7cH1ev/4s1ly5e53yhZZpiH1k3Cecv8eW7DNofQCmzZnP7a+N5+L/vs4GPVbgs5//yEc1pZ7i3LbrBrK/HZdu5SUiIiJSD43q4mmltq2oLC/jh1lzc7rdbIN5OuHwyz9msNt9z7N571UZccD2VJTH73LbUKE62X63XnMVJk2dyfwk442zHTudj5brZOOtzYwbD9qOx9/5mJcmpr6Lkc+ezsYrd+Dmk/7GtKdv4aHzj2vwUJ1Ny/Cfs+cx7uuf2fEvay17rXPbVpy5yxa8e/HRtKhoRtU6PXNc0xLRrBJatsv8UZa823weRW7HBcHtuL4v0LoiIiIiTV6jCtYA/bt14qM/ZuR8u5mG61Sh2t158rNvOeDh/3Hm9oM4Y7tBRd/9O1aLZs3o07UDH0/JT4DMx4RmycJ1pzatuPWwnTj21sf4I80vZ+J18y5k1+/6drX+7/ufs32/3glnnv95+mxWzKD3gDSoJ4GDzexaYB/gOTNb18wuyWbdvNVSREREio573YdkptEF6w26d+KjP6Yve94Q3cBTherf58znqMdf5Zb3PuWBI3ZlrwF9Em+rSEN1xICVuvHBL/G7Cudrpu9E0h1vnSxcb9GnJ/tvMYDjbn0Mz+ATpdC3z8rVuOXx3/7CluuunnD5sdtszI3/G1fv/Uj+xbsdV7zbdkWVr0q2bt4rLCIiItKINLpg3a9bJz76fXrqgnmSLFS7O498PJlhdz3NBj1X5KWT9qP/qnHnCAq2VeShGmDNLu35KsEtzjIZJ51IviYzSxauzxw2iN+mzWDk/97JaN+FkOuJwGbOW0inNi0TLt9t4DpMmTmXt7/6MWf7lPxJdjuufK4rIiIi0tQ1umC9XpcOfD1jNourq4Hc3S4rHamC5PVvTuSu8Z/zyPDdOX27QTRvlnjuuFII1S06t+PDX6fSb8UuaZVfXF3NE5N+oLomv31L6huuK5uV85+jduPSR17i85+KI2Pka2btmfMX0qFV4mDdrLyMv26wJm9O+iHn+xYRERERaSwaXbBuWdGMnu3b8MXU3PZkTBXQ02md/XHmXI7dciDrr5z4NjXNO7YtiVANQQv8G9/9wharr5yy7M+z57Hfk2O4cOyHPPN1/ls/F06bXa8JzdZcoTMX7LUlh1x7P1NnN8ys8oW4VdXM+Qtp36p50jKvffYtW6/XK291EBERESlm8cYfawyyxGpUwToSbvvFjLNuaO7Oe9Nn8eHvU2lVWZGwXKkE6ohJv0+nolk5vTolvtVV8w5tefm7X9n7idfYec1VuX2HTbnx/c9ZXF2T9n7qM1Y73XAdL2AftGk/tu+3JoNOu5bR4z/Pug6ZKuR9n2fOW0iHVi0SLv/qt6nMXbSYv/RcqSD1EREREREpRSV5H+sWndslDUz9unXi7Z//gL6FqU+i1uoFS5by3Jffc/8nk5m9YDGnbbMJw9aP3/JXaqEa4LVJP7Dl2qvVumd4tD/mzueiV95j4i9/cvN2gxmwQmcAVu/Qlse+/I4D1lujIPVM5z7XUPde12bG+XtsyTbrr8Fxdz3FE29+yDXD96B9kq7T2SpUkI7Y+/qHWGflrsxasJAOreMfz+Kl1dzy0nvs2H+thDPWi4iIiIhII2uxjtigWycm/jF92azO+RxnHR2ql9bU8POsuYz7cQrnvziOQTc/yvPf/MyJWw7kjdMOZK8N+1BeVveUl2KoBhj79U80b9aszn2sa9y5/8NJbH/n0/To0JaXjtp9WagG+MdG63L7h18VtK71GXc9ZK0evPnPo2hR0YxNTr2OH3/8JSd1KkRX70TmLlrMf9//nDIzWlTU/X7t2Q8nMej8Efw6YzYnDRtS8PoVAy9vhjdvnfnDGuXHqoiIiIgkkbMWazMbBqwDjHH3CbnabjbW6NiWls3Kefbrn9i5d496by/dYH74Y6/wxR/TWaV9G7ZcZ3VeOeUAVkkRmks1VAMcs8UARoz9kEG3PsaWvVZm3W6dWKV9G+54/3Mc54H9tqNPt44ALIpar2/XjsxYuIi5i5fQJknX+GiLZs6p9yzj2bZcA7RpUcm1Bw/jqmfGcviIJ3jmtINoVl5GRbvU24vWECE6npaVFVxz4PZxZwSfPGUa/7hnNLcftStbaWy1iIiIiEhKaQVrM+sOPObum5tZM+Db8AFwort/AqwPXAccC8QN1mZ2N/Cru59tZhcAuPsF9TmAWM07tGXRzDlcXjWQ4c+/xaCVu9E1yRjS+u4r4tvps/hsyjTeOX5v2oVhMuX6JRyqAbbovSpb9F6V32fP47n3P+frqbN458cp7LF+Lw7ovzZlCbqImxmrtm/DD7Pmsl7X9M5VrkRarlMF7HjhGuC0HTdj3Nc/c9lTYzh/jy2XBeVEAbtYgnSsFhXNWFJdzcZrrFJn2Qff/coWfVZTqBYRERERSVPKYG1mHYF7gEiz7QbAg+5+RkzR14CTgXtTbPIoM7so04pmqm+3juzVpycXjP2Qf/91UM63H9t6+tBHk9l3o3XTCtWlHqhjdW/XmsO33ihpd+vIFx4RPdu34fsGCNYR6bRexwvXZWXGyCN3YehFdzC496ps23dNgFoBu1jDdLTWZWX8/seMuMs++nEK/VZbocA1EhEREREpXekMBqwG9gUiaWEQsJOZvWdmd4Qt2Lj7eHe/2t1/T7G9T4EDs65xBk4cuA7fzZzD6G9+znob6XYD/++n37D/xuumLNfYQnW2erRrzQ+z5ma0Tn1mB48n2xnDu7RtzeX7/ZXrn3+nTvliDNWRY4h+VK3Vgxc//y7u8X3y0++sv2r3BqqtiIiIiEjpMU/zJmxm9rq7V5nZRsDP7v6bmd1L0EX86TS3cTfwLHA6MBrqdgU3s+HA8PBpF2BqWhXMTL62K6Wj1N4D7YHc3pw9f/JR16nuvn2Ot5mQmW14xIF7j7/tqoszXnfj7ffgg48/a/TTqJvZn8APDV2PNJXa33sh6dwkpnOTmM5NYjo3iencJKZzk1ixnZvV3L1rvAXZTF72sbtH5qIaD/TOcP0pwJdAFfB67EJ3HwmMzKJeaTOz8e4+MJ/7kOJWau8BMxvp7sNTl2x4pVRXyV6i/1SKUan9vReSzk1iOjeJ6dwkpnOTmM5NYjo3iZXSucnmvjCjzKyfmZUDuwEfZbGN64ChWawn0lQ909AVyEAp1VVEREREpN6yabG+CHgAMOBpd3850w24+4dmNiaLfYs0Se5eMmG1lOoqIiIiIpILaQdrd68K//2UYGbwjLn7obHbayB57WouJUHvAUnKyyupaZHFZINl5bmvjNSX/t4T07lJTOcmMZ2bxHRuEtO5SUznJrGSOTfZdAUveeE4bmnC9B4QaTr0956Yzk1iOjeJ6dwkpnOTmM5NYjo3iZXSuWmSwVpEREREREQkV7IZY12yzGwYsA4wxt0nNHR9pGHp/SAiIiIiIrnQKFqszexYM3s9fEw0szvM7Meo1/qGRdcHbgSGJNnWBWb2RdS6/QtxDJI9M+tuZmOjnt9hZu+Y2bnh82Z6P4g0XtGfAWa2upk9Z2Zjzexf4WtxPwNiPysao1TnJqrcLWa2c9TzJn9u4lxbjAhf17kx62hmo81sfOS8hK/r3CT4O2vs58bM2pvZ82b2opk9YWaV8Y453dcaiwzOS61r2fC1RnteIL1zE69M+HpRnptGEazd/VZ3rwonRBsLjAAejLzm7p+ERV8DTgYeSbHJS6PWnZi3iku9mVlH4B6gdfh8D6Dc3QcDvcysN8Fke3o/iDRCsZ8BwJXAxe6+ObCKmVUR5zMgwWdFo5LmucHMNgdWiMzor3MTnJs41xa369wse98cDNwf3lu2rZkN1LlZdm7qvNYUzg1wIHCtu/8VmALsR8wxxzsPTeDcpHNeYt9XTeJzmDTOTZwy2xfzuWkUwTrCzFYGugMDgZ3M7L3wG41mAO4+3t2vdvffG7SikkvVwL7A7PB5FcuD8ovAZsAg9H4QaaxiPwPWAj4If/4DaE/8z4Aq6n5WNDYpz42ZVQC3A9+b2a7hsip0btpHCkauLdx9PDo3kXMzDVjfzDoAqwI/oXMTOTfxXquikZ8bd7/F3V8Kn3YFDqLuMVel+VqjkeZ5iX1fQSM/L5DeuYlT5g+K+Nw0qmANHA/cCrwPbOPuGwMVwA4ZbuccW979S/fOKWLuPtvdZ0W91Br4Jfx5OsEXLXo/iDRScT4DHgP+aUG35u2BV4j/GRDvs6JRSfPc/A34HLgK2NjMTkTnJnJuIiLXFqBzEzk3bwKrAX8HviA4Fzo3wbmJ91qjPzcRZjYY6EjwZUvsMcc7D03i3CQ7L3HeV9BEzgukfM/UKuPu4yjic9NogrWZlQFbAq8DH7v7b+Gi8UCmXQSiu/5W57Cakn9zgZbhz20I3uN6P4g0Ee5+CfA8cCRwj7vPJf5nQLzPikYtwbn5CzDS3acA9xH8P6pzE5yb2GsL0LmJnJt/Ase4+0XAl8Bh6Nzc4+5zE5yvJnFuzKwTcBNwOPGPOd3XGpU0zks8jf68QHrnJqYMicoVg6KpSA5sDrzr7g6MMrN+YevibsBHDVozKaQJLO8S0g/4Hr0fJBtlzfDK1hk/sMb0sVqyJgI9gGvD5/E+A+J9VjQFE6l9br4GeoU/DwR+QOfm2qjXoq8tQOcmcm46An3Dv6lNAEfn5tokrzX6cxNOKvUocJa7J/ocSfe1RiPN8xJPoz4vkN65iVOGeOUKVukUGtPttrYD3gh/vgh4ADDgaXd/OcNtnWNmR4Y/3+ruD+eojpJ/TwJjzWwlYBjB2MqP0ftBpCn5P4LJTuaHz+v8n2Bm7aj7WdEUxJ6bO4A7zWw/gm7yewFz0LmJiL62gPj/xzQFsefmcuAugu7g7wAPEjTW6NzEf+1JGv+5OQIYQHDNdA7B++PgmGN26p6HeK81Jumcl3iepHGfF0jv3MSWuZUiPje2/EtYkcYhnF1xW+CNsHujSEbMbMPDDz1k/C033ZDxuoM3H8oHH060PFRLckyfFYnp3CSmc5OYzk1iTfHcxDvmdF9rzNI93qZ2XqD0z42CtYhIDAVrEREREcmEBgOKiIiIiIiI1IOCtYiIiIiIiEg9KFiLiDQwM+tuZmNjXrvDzN4xs3NTrJtWORERERHJHwVrEZEGFE7AcQ/QOuq1PYBydx8M9DKzuPdeT7eciIiIiOSXgrWISMOqBvYFZke9VgU8Ev78Isvv1xgr3XIiIiIikkeN6T7WIiI5U2NlLC6rzHg9xzCz4cDwqJdHuvtIADMbAawdtexVd7/IrNZE4q2BX8KfpxPcwzGedMuJiIiISB4pWIuI5FgYokcmWHZ0GpuYC7QMf25D4t5F6ZYTERERkTzSRZiISPGZwPJu3f2A7+tZTkRERETySC3WIiLF50lgrJmtBAwDBpnZusAB7n5usnKFrqiIiIiIqMVaRKQouHtV1M+zCSYmGwds6e6z3P3zmFAdt1zBKiwiIiIiyyhYp8FiZhVKd1mxMLP2Zlaex+23jpwHC2Q+41MRKoXfbbYa87E1Fu4+w90fcfcpuShXCPqsTLn9RvlZKSIiIgrWKZnZvsCJSYqcGJapzz7+aWY3pFnWzOwVM1szg13cBlyZZJtlZpZwWICZNYu+2DSza81sx6gi9wF7hj9vAHwcfcFoZjtF31/XzA4xs5OT7K9d0qMpADPbDLi8oeuRR7uY2ekNXQlpPPRZWfqflWb2qpltG/Pa381s8/DnFpl+8WBmj5rZhrmsp4iISDFSsE7CzHoARxNcbCUyAjjWzFarx66Who907AGUufvXGe7jpyTL+gMTzGx8+PjGzKZFngMfAH+NKn8XcKOZbRo+vybcBsDOwL/dfXFU+RrgeTM7LXx+FEGwuzvq8YCZ9QqXn2VmD4atOw+Z2R9m9nPUY5GZrZDh8cdlZq+bWVXMa20JLq6vjHqtj5l9ElPuhPBcfW9mx8XZ9tpm9mXU8x3N7Gsz+9PMbkx1gWpm25nZj2a22MzuM7OK8PVNzewrM/vOzLZPVT5B/Z8mGLe7SZL9V5rZiPCcf2Jmm8d7LdkxRNXreTN7xsw2jrP8QjO7IPz5JDP71szWCJ/XK4hJYST7rIz6G9NnZQN/VprZ+2Y2MXw8Fef4loSPaN8DD5pZZ2B0zPGPN7OFUdvvYWbnRj+AdYF/hs/PM7OLzGyAma1oZm+b2ZtmtjSs25vha9uZWQcLvGBmuyb5nYiIiBQFTV6W3EXAaTEXPrW4+yIz+z/gQuDQLPezhCQXi2Z2KRBp9egBzDOziVFFKoAX3T1Ry0YLYF6i7bv7B+GF3LvuPtvM9gN2cveDzMyAM4E3osp/YmYbuvtMM3sjrNNCMzuY4L66c8zsTOBkd3/U3Ueb2XvARmb2V+BPILauzYFfw+2fZWaXE8x2vBjYxd3HRZ2P1xMdS46cBlzj7jPC/a0JPMHy2xphZn2BY4GNCL6getvMJrr72+FyIwgSLaKeX0MwudQ84AVgd+CxeBUws1bh+ocAE4FXgH3M7FHgYeBvwEfAq+HvgHjlgfvj1d/d3cz+AdwOLAvnMYYT/D57EoSBZ4DLYl8zs7XcfU6C4+gF3AAcCKwG/NfMVnV3D5f3IXh/RXoH7Ar8H7CTmb0K/JGgblJc9FlZGp+Vfd29hZn1BJ4zs2EEf8vVBKF+RWBHM+vg7k+GnxFPELSyz3D3rWLPiZlFj+tfFdiX4AuBiJejiwOVwG/u/hswxMw6Ar8Bm7n7onCb7YBZ4bEuBGbH7ldERKTYKFgnYGbNgdXc/YNUZd39fTPrZWbNIxcGKbZ9LnA88Ev4UnegmZltHT7vDLzp7geHzy8EziUIU4fEXtxY0PLZPOr5FwQXkJEL0FUILmAirSDNgEp37xG1mW0IumrGtgwcSnBRdXW47RbAJu4+Jjz2LaL2OxEY6u7fR73WFviruz8etui8DsykbqA8290nWdDNcqC7nxWuvz9wr5nNjyqbSdfObGxNcM4jjiM4//+Kem0f4AF3nx7W83/AEODtcPkRBBeLPcPnXYDZ7j41LP8esHKSOrQBjnD318LybxL8HjcBfnL3V8PXXyG4qJ6YoHyi+uPuP5nZfDNb2d1/oa59gTPdfSkw3symAlcB28S8tjYwPsFxdAdOdPcJBC1dI8NjmxP15cMTUeUNmEHQyrWlu9+Y5Bzl1dIaZ8GSmozXq/E8VKaI6bNymUMp/s/K6pifLXwQ/hv77t0bGEsQ2t+z2sPkr3L3R2LWWUIQghcQtNYviFrehuBzcM8wVEdsR/BlxpXh9l8j6FFT7e6Lw9ea2F+ViIiUInUFT6wX8EX0C2Z2oAXdVH8LW16ifQasnua2FwP3uPtAdx9I0KIX/fxCorrjha1AbQlahU6yYBzfOAu7+rp7tbvPjyq/jruv6e593L0PMAX4PfI8XBZ9oQhwNkFrxbIxkmFr42XA38IgBbA+8ISZ3W5mLUmtA3CGmT0IlAPHhMe/EcEF8UDgIZa3prYEDjezSJfSZsD9BK29kUetrppmdrSZ/WRBN8hLwtd2NLPHosrcEV54Ymbnh7/D54F2MdtqSRCAoxPVqQT3C47VJernvkAkNHcDTgf+HrV8KrCimQ0ys1UIWmZfiXfCANz9D3d/JdyeAVsRzPy8CvBxVNEfgDWTlE9Wf4B3w7on0iXcZluCFuepCV5LdBzvuPtLZlZhZicCE6Jat48M6/9C1Co1QNfwX7VSlYZ4n5UJ/8bQZ2UiHcjzZyVQE4b60UCNu48GXnL3ke5+e1j+KXd/Miy/lOBvsQL4Oeq8Pwu0j3MMDix194/cfUB4DG8RfIFWDZzu7u/GrHMCwe/xbqAVsKxHi4iISClRi3ViHQlazgAws3WAK4DBBN/Cf2JmT7v7pLDIjHCddKTTDLasjJm1Jpj05nF3/zh8bWPCVpaw5cLcPXZsXOSCbxEw28wGu/s78Xbm7tVmdgRBC8mQyOrAse7+eVS58Wa2HnAwQZfGUwlaZ+cDawFPm9lSoGV40fqTBROBXRwe0ziCcY9uZtMtuP9uBbDEzMrD0DXczCKh9Rogdjz1ycD0qOdXh3X+GhgVBr6XgFvMrFl4oVtFcKG9MXA4QYvomiwPnxG1fu/hMbvVndD4MeANM5sbbmcTlk9KdANwkbv/GVkv3MZI4EGC7p0fAJ+b2RnU7er5pUfdeomg9W2Ou48xs4OoHTjnAd1i1l9WPkn9I5K9b+8H/m1Bd+3dgS+BO2Nfi251S+JKggvoPQDMrDtB4B8C7BJV7mHggvDflczsA6DKg9tKSXGK/axM9Temz8qG+6yscff+FnQFfzJswX/MzE5195dJrAYYGrakA6wEnB+nXFuCrtsRkwi+hDiE4AvAudGFzexIgl4Ho9396/Bz4cMk9RARESlaCtaJTSP4Dz9iG+BZd/85fL5STPku4TrpKAMOMbNtwueR7o2R552BV6PKtyO4EOsWdvGF4EJubBiYKoB7gFvi7OsU4Cbgd+AmMxsU1aKCma1FMHY2eoxsR4LWkz5hmfMIuvFd5u73uPtvZnZ12Krwr/AR6d64W2zQCrvz/YegZWkCsHrUBdouBF2i5wBnAY+Hr99nZicQtGLE8xxwTvjzm8ClwJMEF7dzwvq8QdCtcxbwqbvPMbMhwHMejJ9+32ImJKPu7z0ud//IzLYE9iII1f9x92kWTCbWzt3viy5vwUy/ewBrh+fjn8Ap7l5rkrRYZrYywfjjyDjoGQS/m4iW1A4WseVT6UIwOVG8Y7zNzH4HNgUGAPuH3VRrvZbOTtz9FDO7C3jFgonJbgDOd/fp0aHf3UcAI8LW7d0JvogYSvAeleIU+zeT6m9Mn5UN91lZq5da+CXBmQTDRZIFa4Ax7r5bWP8LEpTpAPxuZiOAfgRfaDQnGFf9Qvg7WJvgC+rfCMak/w34X3jcGwPvp6iHiIhIUVKwTuxbYJ1ECy2YWOaHqBbr9YDv0tx2BUF3xjPDbZ0GdIl6fihBV14APBiP1sfMVnf378IyS919s7C1Ylq8rnNmtgNB6DvN3Rea2SEEF3b/iNr2VwQXOtHrLZuQJ8GxlwFvmtnOBC2mi2O6TkfGMpZFtQzdBpxHcEH1GMEY5Zvd/W9mdj1wZ1QL0wCCXgHNCFqFDiVoxbwb2I+gReqIqN3tQhC+tgUuN7O+7v5nuJ9hBJPgRLqFx44jrFVvDyZYahXV0p2QBxMZ/UQwG3Jk8q3dgE3MLHJP4a7hzycTvF8ikzuNJ7igTCjsvvogcHXk3BBcbEffBmwgQet8ovKpDAb+k2ihuz8RtqptDfw30WtJjqE/Qev5Nx5M5PQrwQROOwJVZnYjwZcD5WbW0d3/EX5h8TrBFxHTqP1FghSf2M/KpH9j6LOyIT8ry8NQX0k43trdnweej1f/KPGGjcXrBtMH+M7d/xl1fOsTTAa5fdh74CYPZ2o3s3XcfYmZDSb4gvS/4ZcLrcK6Fv29z0VERCI0xjqB8CLni7BrHgTjYXc0s5UsmLH03wSzumJmQ4FP4nUvTOBygnF6idxDzKy5YTfF98ILkGg3AndbzK2bzGxv4FaCYBK56PsbwS2W7rD0xvwlsjPBbM0tCIJe5FYzke6N4wmC40lhXdYnaMV9m6C1ambYqvytmXUiOI/R524vgjF80RPtxKoOt90K+JSga/X5QKRrNgRjdzcjCIBPh6+9Bwwzs/bhRekGcbY9muCiNB0nE7RWR2bpPcbdu7j7Cu6+AsFEYysQBIktzKwqDJunhvVO5kaCIHBd5AV3nwL8acGta/YkCKgvJSqfTNiK7u6eaubtswgmTPIUr8WzAfAfC+5/ux5BV9XJ7t426hz9g+DCOxJiNnD3TwjCWXs01rqoxfmsTPg3ps/KhvusDJW7e39gBzL7Yr0aWNOW31ZsD4Iu7bEGEzPePpq7v8fy7vNEvQ/6EHQjvzh8XkNw1wUFaxERKRlqsU7unwTjz3Zw988tmKH2LYKJZa4LWyzbEUxas0e6G41tsSD4gqMsarkT1eJjwWRXLwBnuPs74bf4kQuO4QStDfdZMP62S1ifjQnGFVcTdIP8yd3/Z2bbEbRmTDaz3d09Xre7WvWJZsEYxYuB4z2YSXq9qGUTidO9kaAF5Ybw5+ZA76jujTsRXGQONrN+4XEdTHBbqlbx6hDN3eeb2S3AJwStW6MJLuwjrc8/Elyozgpfe8vMHgK+Ihj/93mczV4PvGxmr3n82bIjx9s1rGu8cB5bz3FmdjPB2OFWBN0ur02y7Q4EE//MiGr9vt/dTyW4+L+JYEz3Ye7+e4ry8bbfLNxG7CR8seV2IGhlez7Fa6sSdP+NPRejCCZf+oFgbPl+nuSWTBbceueH8OkLBO/vTROVl6IR/VkZ929Mn5UN+1kZigyp+IGgq3akPm0I7mCwMrVvZ1ZB0Jo+gWAytthjbM/y8esrE/QeOCxqeTOC7vLLwr27V0ctb04wCdzpwN7hF4e4+0LgNgtmVm+DZgUXEZFS4O56JHkQtHYOT7L8aGDreu7jQuD6JMsfB44Jf94UeJTgPqCR5e0JAkxngm6zpxFMiBNZ3hdYMWabuwPNEuzvMOCxBMt6AiMSLPuCYAxx9GvdCe7rmmhfwwkuvi18vj7B7V0GE8wePIUgKP9IcEup7wkmt/mFYIKwfP3e1wfOa+j3Xx6Pb0eC2xE1eF2K8QFseNAhh/rvM+dm/NigX39v6Po30DnTZ2XtZSXzWUnwJfurBGPPW0S9/jEwKME6GxO0Wt8VPu9K8MVZdJlLCFrsj0mwjXvDuq6bYPlkgjHvqzT0+1sPPfTQQw89Uj0i/0EXlbDL24bAhx7e9zdOmWEE4/rGePBteqMVPd43bNHbm2AW1Z+Tr1n8zGxz4A9fPla91vGKNAQz2/CgQw4d/68b/p3xutsO3YyPJn6oLqwNQJ+VBa1POUFvoBkpCyfeRluC3i9x72luZp2BGV6354KIiEjRKbqu4OHF0LMEM5lea2ZbEdzmal2CrqaXhEXXB64jGIcVN1ib2d3Ar+5+toWzmLr7Bfmsfz5EXziFFzEjG7A6OeXuY+O8plAtDa66BhYszfyLx5ri+66yydBnZeF40KU761AdbmNOiuXpzh4vIiLS4Ipx8rINCG5DdCnwP4IxW+XuPhjoFU64BPAawcRRj6TY3lHhOC0RERERERGRnCu6Fmt3HwNgZlsQjOHqxPLw/CLBLM+T3T0ym2oqnwIH5qGqIiIiIiIiIkXZYk04k+u+BN3MnGDyFYDpBBO8ZOJmgklzRERERERERHKu6FqsYdktVI43s4sJ7tN5e7ioDZl/GTAF+JLgdiqvxytgZsMJZlxl9d5rb9i8xfLblpZnMQVRmaW/UlkaRdMpY+ncjaQmjflfPNntUMN9pbOdDLeZeNXSGW5t5Un+nGrfOhcvK8Pd+fyrr+ncuQvdu3dn0aJFfPXVV/To0YOOHTsCMG/ePCZ9NZlW3VejxsuoqXbW79Ehj0eRX3PmzGHydz/jSxeCGVbWDK+pAa/GyivwmmqoWUpZ87aUt+4E4d/Skmnf/8/dt2/g6kuULl26eM+ePRu6GiIiIiIFM2HChKnu3jXesqIL1mZ2BsHtUe4luB3KFQTdv8cR3HdzUuK1E7oO+IAEwdrdRxJOcrN+v7/4I/8bk/aGW1ekn/NbNksvcLdMY5uVNQlvBVyLLZ6XskzZwqTzxyzf1qLU21pmwez0y0apnlW6c9WUt++culDLdrWeTvr5D/557S2MeWscDz30EF26dGHYsGEcddRRHH300Zz+zGe0HHULP335Kb32OpeF85cwa84iJt+8e56OIj/cnSuuuILzLr6aDhvuQ4uVN6BZmy7Lli+Z/TtLZ/9Gs7bdKKtsw+wPH2bR1O9o23sLypo1Z+pbd3VJsnlpAD179mT8+HRG44iIiIg0Dmb2Q6JlRResCQLuI2Z2JMH46CeBN8xsJWAYMCjTDbr7h2aWflqOI5MADemHaCh8kAaF6XyonjUtdbiOnKMwYK+1xurcf8dtvDZuAvvttx8jR47kjTfeYIsttuD9KQsYNGx3Zm+/O58+N4qF03+jRacVAeh9/BMlE66nTZvGCSecwBOvvM+qe19ZK1BHNG/dBlZcY9nzVtuezJJfxzN/ymRq0nyvioiIiIg0lKIL1uEtUraNfs3MqsLXrnL3WRls69Con6vSWafMrMFDNOQ+SIPCdFEJz5sRjG3YctCGPPX4o+y21z688MILXDZiFEftMYxOHTvQs++mDNj3eD4adQZr/+0qWnRcASj+cP3ZZ59xww038Oijj1K++qb0Pvhqyiqap71+i79UEYzggGkTnslHFUVEREREcqLognU8YdhOdVutvMskQEP6IRoaNkhD0wnTNXNmpixT1rZD1ttPq9U62oLZy8L1gHXXZLUeq/LDr79TNXgzTjz7Am466wRuePljttj9IOYtWMIXd51C36OupEXbVYHiDNfuzrXXXstVV13F8ccfz+pHjaCiTcc65RbOXwJAi1YVcbfTsm1lXuuZypIaZ96SDOcTAGpcN7IWERERaWpKIlgXUpllHqAhPyEa8hekoWmE6XSCdLJ16hOy0zVn7jzOufhaZsxZwMKFC9lkow0pb2YccczxPP3I/Xz3wVt0WncQG+58AJVt2jPxphOoaN2BVbc7mlY9Niq6cH3hhRfy3//+l/Hjx7Pqqqvy6nV1R2EsmLN4WaBOFKDbtm7YYC0iIiIiki4F6yxkEqIhP63RkOcgDVmHaSh8oM4mQKe73XyH67nz53PLXfezRs8evPHic3RqVcnisjJaL3VOO/cizjhhOCdcdxfdOvWi75Y7ssKAKr598wV+fecx+vQZAkC/M0fz0RU75LWe6fjpp5+46aab+Pzzz+nePbgzXqqAnGh55zbpdxsXEREREWlIRXkf62LQsqIs4SOZyprFdR6J2OJ5tR6plC2cU+uRDls0b9kjLQtmL39kqHrWtGWPXKqZMzPlI58y2X4m3cAj52nFts355MXHWLhwEVdedQ1fffYREPSc2H67v3LSeRdzz8X/R1ebS7e2zenavjW9Nt2OhVN/ZsGvH9GiVQUtWlWwyUUvZXRc+TBu3DiGDh26LFRDEJAjj4i2rSuXPaKXRz+6tV3+EBERkSJlFv8h0sQoWMcos/Rn6c4kRENmQTo2RKcTpKNDdCmG6YYIzQ0l9nyts+bqvPnYnVRWVrD1Tnvw1y2reO3F0QDsvd8B7LrXvlx++G40+3MyfVZsS9cObdjyH1fz1YNXMHXCEzRvabRsW0lVnG7XhbR06VKaN08chFMF6OhHpzaVyx4iIiIiIsVMXcFTyGQ8dLR8jo2GLLp1RyuiLt6NNTgnE30Ol012tmA2q660AlecdizHHnoAJ19wFWeefjr9BjzMeZdezXEnnUrrLt156JrzOfvuZ+izYlu6tR1M6wvv4o2RF/PLG4+yyd+vwzqsStV1Y3j95KENcmwtWrRg/vz5tV5L1eKcKDh30RhrERERESkRCtYxDM/b5GIReR8bHauIgjSUZphOZ6x1Ot3A0zmfPVZeiUdH3cG8pcYFl13F9ltswkXX3szue+7Df666iDm//UCXFVcLCq/Ri7bn3sZ/T9kV3JeNV9555Ds8M3xwyn3l2pprrsmHH37IokWLlrVcp2pxThSg27WIP1u4iIiIiEixUbBOUzYBOqKUgjQoTBdadKs1Ldthi+ZRBrRu0ZarLrmAzbbYnPPPO4/HX9mO7XbZg7efeYS9/372svWXLFrI3D9/pXv3brSKGsd86AMfcPcBAwp6LH379qVFixbLxlpHpNv6HBum2zfXR5SIiIiIFD+NsY5VU1NnLHSm3brrOzY6Y9FjpOtxW6x8TjwmyS077+HvzxbNo2zhHGzxPHbcuopffvqJJXNncsSxJ/DyEw8x7ftJtGtRQZfWlXTv3I6/Hn4Sz569H188fjOtls5ZNk759Gc+K+hxTJw4kfnz5zN48PLW8mQt0rGPiPbNmylUi4iIiEjJ0JVrPWQzNhoabnx0rHzdEktBOjuJWq6btYDy8jJaNDP6rrkap59zPiMvOYsRj45mRvNmQcA+6u9sudMePHX3bYw6aU+OvvEhuqwcdBe/4rXJnLll74Icww033MDxxx9PZeXyMJ1ul+54QbpTy4brDr60xpm3uDrj9Wo8D5URERERkaKmFusU4rVAZ3K7K6hHi3RsS3QOungX4pZYTVEmt9lKJrblGoL3YPXSal577kkqrIbDDjuM+XNm8+UH79KpZQXtw3C9+uqrc/hZl7DLkSfx8IUnsnjBfLq0rqRL60r+894POalfMjU1NTzzzDMcfPDBKctGWqSjH9E6taxo0FAtIiIiIpIJBetYXp1VeI7I+pZXkNcQna9x0005TOdLdLiOvH+evvc2HnzwQbbcfFO+nfQ5fTfYgEfu+Q/vvvY/OrWsYPUOLZcF7F0OPIw+/Qdy+0n788fPywN1vsP1F198QYcOHVhllVVqvZ4qRMPyIB0bqFtXltO6sjyv9W4qzOwOM3vHzM5NsLy9mT1vZi+a2RNmpmnZRURERNKkYJ2leAE64y7eOR4bnc8QDXXvM92UpJoRPNeif4e2aB6bbTKQlx++kxOOOozddt4Jr6lm7bV6c8W5p/PKU4/SurJ8WcDu0KKCUy++hm33PJArj9yDyeNeWzaG+ZGPf81bnSdPnsx6662XslxsiI7XMq1AnVtmtgdQ7u6DgV5mFm9swIHAte7+V2AKsH0h6ygiIiJSyjTGOoV6z9Adqwhn7E6kqYXnYlM9axrlsGy8Nc1bc+geO1C1xWbsceBhdOzQgVEPPcreu+3Emr16su6Gg2qtv++hR9G3X38uO+kols6bzV/32C+v9Z09ezbt27dPuDydrt3xwnTrCn3/lwNVwCPhzy8CmwGTowu4+y1RT7sCfxSkZiIiIiKNgK5YY1hNTf1m6I6nSGfsjhbbGq1QnR+ZjsWunjWt1kzhAD27tOPVZx7n668m8eLop7nupls49rCDaF1RFjzCcNqpZQWbDRnCLQ88wd3XXcqk997I67jl2bNn065duzqvpzNeOlELtUJ1zrQGfgl/ng50T1TQzAYDHd19XJxlw81svJmN//PPP/NTUxEREZESpBbrXMvBrN2FaJUu5uBck+T4y3I0SVgpiW259uat6VAJN155CVtsvzObDBpM//79adnMwjVqh9H11l2HG/4zir8ffiA33PMoG/Woyks9Z82aRdu2bdMun6yrd1ML1GbWHXjM3TcPnzcDvg0fACe6+ycJ1r0DWBd4zt0vSbCLuUDL8Oc2JPhS1cw6ATcBe8Zb7u4jgZEAAwcO1PznIiIiIiEF62zl8LZXkN8wXYwhOll4Tne9QoXsQo+vTijqNlzevDXr9ujOcUcexpwFi7j0oguxZWG0hujcNG9JDQM2HsT5V1zLmcccwq6ffRK3Zbm+Jk+ezKabbpqyXKqx000wVHcE7iFoVY7YAHjQ3c9Ise6ysdNmdqeZ9Xb3yXGKTiDo/j0O6AdMirOtSuBR4Cx3z/808iIiIiKNiIJ1MjkOz7EiYfr+58ewaMkSDtlxS8rLs5+wqRgDdES2QTrdbTb2lux497j25q256JRjqWnRFliK1yxmcVklLSvKCMJ1RBBUd999d94d8wrnn38+119/fc7r+Oeff9KhQ4eEy7MN1Mtb4hutamBf4Kmo1wYBO5nZlsAnwNHuvjTOulWkGDsdehIYa2YrAcOA/czsEnePniH8CGAAcI6ZnQPc6u4PZ31UIiIiIk2IgnUsr85roI5tmZ6/cBGHX3wTAEMHrM8aq6wQVMMds8SBophDdEQ+wnShpdNanat7WKeSKFyXLZxDTYu22OJ5VEZukFQR/LBgSU2tYHrk8Sdy2D67869//ateX+LEM2zYMJ544gn23nvvWq/Xp4W6IUP1oqU1TF+wJOP1ltY4ZjYcGB718siwGzVmNgJYO2rZq+5+Uczf+/vANu7+m5ndC+wAPB1nd7FjpwfEq5O7zzazKmBb4Cp3nwJ8FFPmVuDWtA5SRERERGppWn0uG0iyycdatWjOj8/+h6evPYdeKwfzCV0w8kFabLo3T7xWe+6gUphYrGbWtGWPpqKQM7VH3+Malk9oFrnvui0OnlfWLAagZUXZsgfA+n3WZs011+SSSxINxc3egQceyOuvv84777yT9jrFGqrry91HuvvAqMfIqGVHu3tV1OOiOJv42N1/C38eD8S7PRakOXY63O8Md38kDNUiIiIikkMlG6zNbJiZnWJmGzZ0XaJlc0/p7p06sN2gvyxroV6xSycA9jvnGk64eiQ9dz6SH7/5NtkmGlRDh+l8dQMvmrHVMeKF6+iAHR2uIw8IQnarynJuv/12brrpppzXq2PHjlx88cVcccUVaZVP1vU7OlTPnDkjJ/UrMaPMrJ+ZlQO7EdO6HCUydhqCsdPf579qIiIiIhKr6IK1mbU3s+fN7EUze8LMKs3sDjN7x8yixwOuD9wIDEmyrbvN7LLw5wvM7IJc1DFeeM7l7bCO3mM7vn3gGr4adRU//fIbv02bSe+DT2edQ87kjY++zMk+6quhw3S+FWuojogN10CdcB0J2ECtgP3MM8+w9trRPZFzZ6eddmLMmDEsXRpvOPBy6Y6nnjZtKltsMjBn9SshFwGjgInAO+7+spmta2axXQ2eBA42s2uBfYDnClpLEREREQGKMFgDBwLXuvtfgSnAfoSz3gK9zCzSJfI14GSWT9yTyFFm1iKTCiQLzvnu9hvp6r1i5w6s2q0TT1z8D2Y/dxvbbbQ+30+Zyv0vp9/NNqf1igrSxRSm89Fanc9Qncv3T6pwDSwL2JGQPe3XH7nrrrvYfffdc1aPaN27d2f11VfnvvvuS1gmk0nKRj/1X6q23Cpn9Stm7l4V9fOn7r6Bu/d193PC1z6PmWwMd59NMIHZOGBLd59VwCqLiIiISKjogrW73+LuL4VPuwIHUXfWW9x9vLtf7e6/p9jkpwRhPb39VydvacuXZGOnK5o149ELTmDSqCuZOmsOe5x3I+9/mb+u4bEhupiCdL4Ve0t1rFThOhKwIQjZX3/xKV9//TV77bVX3up03333ccYZZ/Dyyy/XWZZJqG5ZUcZvv/5K77USDS8W0NhpERERkWJQdME6wswGAx2Bn6g96233DDd1M3B0in0NN7PxZjZ+6oz83mIrWiaTkVU0CyZwf/69T+i1Ujf2u/hWbn3q1dzVpQRDdC5bq8vadqhXqE6nJTpfvR1qheuYSc1gecAuWziHzYcMon///jz9dLwJpnNjvfXW49FHH2X//ffn119/TVk+0SRlC5bU8Nuvv7L6aqvluooiIiIiIjlVlLfbMrNOwE3AnsAppDnrbQJTgC8Juku+Hq9AOGPvSIAN11nDM65wBuozo/fkn6aw1irdufqYfTlo2yEMPv4idhjUj9W6ZxcwSylEx8pVqM5lC/Wy22ElWFYfkfdNovrW2nfU7bgivHlrAMoXzeXOqy9g890OpH///myxxRb1qlciW2yxBXvuuSd33HEH5513XlbbmDNnDqNHj+aqq67Kce1ERERERHKr6FqszawSeBQ4y91/IDez3l4HDM1JBTMQ3SKdi9tkbfmXdfhuylROH/EwO599LUfuMJRuHdqmrkecrt1NPVTXt4U6kXyPy0/2Hqq1n5h7sUfPHL5Gzx7cffX57L777pxwwglMmZKfHsQnnngiN910E19//XXG63711VfstNNO7LTTTqy88sp5qJ2ISBbM6j5EREQozhbrI4ABwDlmdg5wF8GstysBw4BBmW7Q3T80szG5rWagkPeULisr46BthvDmJ18x5vqz6bVStyAkLyxYFRpcfUJ1qY2fzkadlmuAlu2WLY+E6+22GMJn/3uEqx54jvXWW49TTjmFs88+e9kt33JhvfXW44ILLmCbbbbhgAMOoKqqiiFDhtCmTZu69a6u5uOPP2bMmDG89NJLjBs3jvPPP58TTjghZ/XJ1OIaZ9aizOdcqPa8dnoREREpHYmuK/R/pTRCRRes3f1W4Nbo18zsaWBb4KpMZr1190Ojfq5Ka6Xq6oKG5UzddNiOy34u5VbnTDXlQB37fqyZMzPpMdXpkh7det2y3bLnXVuVc/WRu3DSSSex2267sXDhQs4//3wqKipyVvdjjz2W9dZbj1deeYVLLrmEzz77jKeffpoePXrw5JNPMnbsWL799lsmT57MSiutxBZbbMGhhx7KqFGj6NSpU87qISIiIpIX+fjyQF9IlKSiC9bxuPsMUt9Wq1FrSiE6VrahutQDdTIZh+uIBXUn51t57rc8//zz7L333nTv3p3tt9+eHXbYgTXXXJO+ffvSunXrrOtpZgwdOpShQ4ORGC+++CLDhg2joqKCnXfemV133ZXevXuz5pprKkiLiIiISMkqiWDdlDXlQA0K1cl6T0Qvi3e8sWO7o4N27LKOs97k5Sv/wa9/Tue5t8bz+Ijr+WHKnyxcvJQX33qXVVZZJav6x/rrX//KN998Q4cOHXLaMi4iIiIi0pAUrIuUAnXTDtSQ2fj9VLOGQ3ozk6/Qoowjtt6YI7beGIBrHn6e/uutwz9OO51TTz2VVq1apV2nRLp27VrvbYiIiIiIFJOimxW8qSv1Gbvrq6x954xDdWSG78YUqrOV7ezzidY7bd9hvHXTuUx86Wl69erFOeecww8//FD/ioqIiIiINCJqsS4CTTlIg1qn46nvBHqx68eeq0y2v9oKXbj/3GP48sdfueuTqQwYMICNN96YTTfdlAEDBrDNNttQWVlZr/qKiIiIiJQytVg3gMZyP+lsRVqls2mdXraNHIbq8vadlz2KQT5mpc/F/dT79FiJK3fcgB9//JHDDjuMuXPncvnll7PWWmtx2223sWjRoqTrz5s3j88//5yZM7Pbv4iIiIhIsVKLdZ41xeAM9bs9VtLt1iNQpxOcY8ukMy45l4rhVm+p3rM2+mZ2Mthlq96wVW/Gff4NV9w7kvPOOoOhA9Zjm80Hs8dft6Bzh/bQsh1vT/iIq267m5fffJceK6/IlD+nssmAfhy4564cfPyplJXp+z0RaeTi3TpHt80Radp0S61GR8E6x5pakM5XgK6znywCdS5aoMvbdy5IuG7oQF2f9+2gddfgyQuO5+d5SxjzwWe8+Na7nHntCDZcb21+/n0ai5cs4dSjDubBmy6nZYsWzJu/gBfensA1N4/kjvse4oHHn8rZrOO5tHhpDbMXLsl4veoa/YcoIiIi0tQoWNdTUwjShQrPdfabZet0rrt05ztcFzpU5+s922OFrhy8QxUH71DF3LLmjHl/Ij179Wbd3qvTrNnyj5rWrVqy5zabsduWg7nqtnvYYsggXn79DXr16pWXeomIiEgJUk8PKTEK1gk0hcCcSEMFaSieMF0I+QrUxfDebd+2DbtstVnwpFn8j5ny8nLOOv5wOrWuZOimg3jx1TGss846BayliIiINBh1hZZGRsE6hlcvLYpgUmgK06n3lctW61yH6pJ9zy6YzdH77kLrli3YaovNeOb5/zFw4MCGrpWISHFQ8BARKRkK1k1YQ4TpXMzm3VCt07kI1wrUURbMXvbjQbv8lVZUM2zbrbnkiqs46qijNKmZiIiIiJQMBesmqBCBOtf3mC7Frt6xchmqiyFQ18yalvC9VD1r2vLfWSRAt2yXcFvVs6ax69CN6dNzZY685F9cdfEFbNi7B61bVLJFvz65rnqTZGZ3AOsCz7n7JQnKdAcec/fNC1o5ERERkRKnYN3E5DNU5zpMQ+MI1ND4QjVk8V6KaqGOFt0LYO3VVuaNkZfx/ueTmfz1t8yet4BHXnu3PtUUwMz2AMrdfbCZ3Wlmvd19ckyZjsA9QOsGqaSIiIhICVOwbiKK8b7SyTSWQA0NfyuthlKr1TpJmVhmxsbrrcXG661FzZyZHL3zlrTc7sh8VbOpqAIeCX9+EdgMmBxTphrYF3gq0UbMbDgwHKBHjx45r6SIiIhIqdIgxiYgH6G6rG0Hheo0aEx1YumMV8/Xe6wJag38Ev48HegeW8DdZ7v7rGQbcfeR7j7Q3Qd27do1D9UUERERKU1qsW7E1ErdsBSqE7da5/O+4LmycGk1U+ctzni9pTVFOVvvXKBl+HMb9KWqiIiISE4pWDdS+WqlzoecB+roSbISjOvNRkOFwWIM1Jm+v9I9d9FfRkTeb2q1zokJBN2/xwH9gEkNWx0REZEio9vbST0pWDdCTTpUQ07DdEQmoboxTlRWH9mE6shzheqceRIYa2YrAcOA/czsEnc/t2GrlYbGcqHTWI5DRERE4lKwboSS3QYp623mKeREQlexdgPPtJU6V6G6mAN1Q9z/XOrH3WebWRWwLXCVu08BPkpQtqpwNRMRERFpHEo6WJvZMGAdYIy7T2jo+hSTfIVryE/rdbEF7IYK1FC8oTrfgbqsbYe4XcElN9x9BstnBheReNSzQEREslS0E9iYWXczGxv1/A4ze8fMorsurg/cCAxJsp27zeyy8OcLzOyCfNW52OQroNXMmZm3W0hVz5rWYGOZI/vOtNt3Ls5Fzaxpyx7FqNCt1ArVIiIiIlJKijJYm1lH4B6CW8RgZnsA5e4+GOhlZr3Doq8BJ5O6FeYoM2uRr/oWs3wGtUIE7HyH7Gz3k4tjL/YwHZGPUJ3s3ClUi0iTYhb/ISLFQ3+nkoZi7QpeDewLPBU+r2J5eH6RYHbbye4+HhifxvY+BQ7McR1LRj66hdfafh67iEPtbtn16Sqei5Ceq9bpUpDL90y88xb7Wj7vjS6SkXgXS/nsCqzuxyIiki79n1G0ijJYu/tsAFv+xmkN/BL+PB0YkOEmbwZOB0bHW2hmw4HhAKt2ahevSMmLhLlCBOxouQ5KDXbLq3oE6lIJ0hHZvkfq3YKf5y9oMrVoSQ3T52Z+H+vq4ryPdeOkiwsREZGmpYj/7y/KYB3HXKBl+HMbMu/CPgX4kqDl+/XYhe4+EhgJ8JeeK/qS2Ylv11TRrrSDd3TIK8S42URhq1jCUyK56OYt2Sm2gC0iIiIikkqpBOsJBN2/xwH9gElZbOM64APiBOtMJAvd6SimYF7okF1r30UUuJvCLbJKkQK2iIgUVBG3hIlI8SuVYP0kMNbMVgKGAYMy3YC7f2hmY3JdsUzFBvNiCdqxobCh7lWcj0mt8jXBmoJ0YeTr9yciIiIikitFHazdvSr8d7aZVQHbAle5+6wMtnFo7PaKSakE7YiGCtxQHAFLYVpERERERGIVdbCO5u4zSH1brZIXHbSLJWRHSxUsGzJ454OCtIiIiKibuIikUjLBWkpDsXQpz5aCdOn/DkWWKfRts0RERKTJUrAuUsXYWp2NZEG1QbuVK0CnrVDnKvb90FR+R2bWHngIKAfmAfu6+2IzuwNYF3jO3S9Jsn5a5URE6kUttiIiSSlYF6HGEqpTyWfoLoZQ1phmkC+EYvidNZADgWvd/SUzuxXY3syaAeXuPtjM7jSz3u4+OXZFM9sjnXIiIiIikl8K1kWmqYWpREolZNU3POdr23of1d+CJdX8MWdRxustqa7BzIYDw6NeHunuI+OVd/dbop52Bf4ADmD5nBIvEtxuMF5grkqznEj68tGFXq2dudcUzqmGc4hICVGwFslAPoN0LqVTz2IP36VyruMJQ3TcIG1mI4C1o1561d0vMrPBQEd3H2dmRwG/hMunAwMS7Kp1muVEREREJI8UrItIsQedpqqUA14yyY6rUO/Fxnpuk3H3o2NfM7NOwE3AnuFLc4GW4c9tgLIEm0u3nIjkm1pXRSRWU+hZIssoWBcBBeri0xQDX7SmfvyFZGaVwKPAWe7+Q/jyBIJu3eOAfsCkBKunW05ERERE8kjBusAUoouXwmTxWDRjTkNXoZCOIOjCfY6ZnQPcCjwJjDWzlYBhwCAzWxc4wN3PjVq3TrlCVlxERESKjHrPNBgF6zxTkC5eCtINr4kF6Ljc/VaCMF2LmVUB2wJXufssYBZwbsy6s+OUExHJTil1Wy2lujYGOt8iKSlY55iCdPFpKgG6MYbUhdOaxu8uHnefwfIZv+tdTkREpNEEZLXKShFSsK4Hheji0hQCdGMMz9GacpAWERERkdKlYJ2EgnNxauwBurGHZ1CAlhKm1h4RkdKlzz7JIwXrGFZerkDdwEoxODeFMBxRqFC8aGbDntNFS6qZNndRxutV1xTnf9BmdgewLvCcu1+SbRkRkbgUWESkiVOwlgbz/+3dfbQcdZ3n8ff3RiA3CQ95QEYMmQc3BBiEGLMOYdFcFVzCAXcWOIAKCCsTFjwzA6JHkeicAwwyLGLQneTkjjzDOCCOWR5dHoSIA5k1GePIukR8AgIqeSIhD0By+7t/VHVu3+rq7uruqu6q7s/rnHvS3VXV/avv/d1Kf/pX9esiBmgoVoguwshwtwN0PzCzU4Fx7j7PzG42s5nu/nyz61Rau3YtQ0NDYx4744wzuPjii9mxYwcnnXRS1TbnnXce5513Hhs2bOD000+vWn7RRRdx5pln8tJLL3HOOedULb8MOIXgO8UujLw2BDO7HQ+sWbOGSy65pGr5NcCxwNNPP80Xv/jFquWLFy9m9uzZPAZUfaowNMSyZcuYBdwPfDVm+R133MEhhxzC3XffzdKlVfPRcS8wDbg1/ClvV/YQMAFYQnjRfGQfn3zySQCuv/56HnjggTHLBgcHefjhhwG46qqrePzxx8csnzp1Kt/5zncAuPzyy3km0rbpwJ3h7UuANZHlhy5cyPDwMAALFy7k5z//+Zjls2fPZvHixQCcffbZrFu3bszyecBXwtunARvLC8J9/PCHP8yXvvQlABYsWMDOnTvHbH/yySfz2c9+NtxkiKjU+h5Q3fPgsvvv55RTTgn6XnTh0BCLFi0K+h5B/SqXQUXfA74YWQawGJgNY/texfJlMLbvRWpwB3AIcDewNKY+9957L9OmTePWW2/l1ltvrVr+0EMPMWHChNG+F/Fk+O/1wAORZYMLFoz2PWBMzxsaqu57z4ztfdOnT+fOO4Ped8kll7BmzZoxyw8FhsPbC4GfR/ZvNkH9AM4G1kWWz5s3j698Jeh9p512Ghs3bhyzfEzfA8b2PDgZ+Gx5d6h2xpIl2R73Lrss6Htr13LhhVW9L+h7xx8/9ri3YsWe5WP63vz5VdsvJux7jz3G1VdXf566bNkyZs2aFX/cA+546aXRvhddODQUf9wLl0HMca9iGcT0vYplg8DD4e2rgMcjv/uGfY+K494ll1Qf94j0vcjy2UT6XmT5vMsvjz/uhfvxYeBL4d0xfS/cj9i+V7GPZwAXAzuAkyLLAM4LfzYAp8ccFzLpexWuueYajj322Mb/5zbqe/ffz1e/Wt37xhz3oGr/Ex/3lizhnnuqj3z1/s+tRcFaOqaoQRqKEabzFKIVlnNniNH3LI8QfPd1NDQ3XMfMFhK8v2CfffbJpqVRlW8EL7sMTjkF1q6FCy8cuwxg0SI4/ngovzGPLr/mmj03K953Vnv0UYj5Tx4IRuDuvx9i/pOfMaPOLsyfD/feC9Omwa23Bj9jVgAeeggmTIAlSyD8T76ynZUDgvPnV+9DefmVV8bvn1nFAGK0NtOnj97+678erWHMc8TuX4NlfOELEIYbTjsNKsLNihXBz5e/XL1teT9WrIDPfS5+WXn5pz8dv6y8/PzzYf36+NqsWAFnngm8+CLEvMHc47nngr4Xxz2oW8wbTP7lX+DYY+HppyH6BnP+fFi8GGbPhsceG9v3ygVctgxmzRrb9yp/h3fcAYccAnffDUuXVu3jgQcG/95yS/z+T5wY9o2///s9fS92/66/Hmq9wXSHq66CyIc6UL9v3HVX8BO3jPnz4dBDIfxQh4ULofyhTnml2bOD+gGcfTasW1fVN669Nrh96qnxfePLXw73/8QTIfKhDiefPLp/MeGk1v6Vm3j++cFP3DKAs84KfuKWlf82ai2r/Ldy2Zi+cc01Y/te9G+/ou/F9Y3DDgtrc999scc9IFgh7HtV1q+PP+6V2xlz3BuzPAw3VX1v/nwYHITwQ53Kvhd33PzCFyq2K5s+He68c/R+tDaVfe8v/mK075XNnj26/2Hfq1I+6EaOe0BwsA4/1GHBgrF9b/78oO+FHyiO6Xvldp5xBlx8MezYAZUf6pSXn3de8LNhA4Qf6kT/Ns46KzjsRZeV75ebX+u46Q7veU/1srJ6fxu1tisfww87LH7Znv2LHPfywFyn6Ywx513T/YfX/lW3m1F4RQ7RZXkO090I0VmH5R0bt9dcNvvb31vt7nMzbUAFM3vvjOM+uuqoc77QeOWIp/72fF574bka/5V0R3iK99fd/Sdm9hFgjrtf2+w6lebOneurVq3KtuEZyuKs1Swuv673nK0ua0e9urVa0yz2sRu1yZO87X8W/abZ1ys/b57+Tmtt2+6yVvXK34auSqgtzf5WXt5vx2kzq/meVCPWkgoF6WxlEaQ7PapcLzhL5rYRnDUHMAkYaHEdkcLLwxszERHpPQrW0rKih+l+CtKdCNFZBec3Nr+RyfP2mdUEp3avBI4muEy5lXV6hsKViIiIpKmngrWZLQAOB1a4++put6cXFS1M5zk8VyrSiHSaAVqhuWOWA0+Z2cEEc6ScZWZXu/uiOusc0/FWioiIiBRUoYN1zFfDHAl8DbiIYPRF2lCkEF2UAF2W5TXSaQbqtEK0AnR3uftWMxsCTgCuc/ffAT9psM6WDjdTREREpLAKG6zjvhoGeAK4FLi9u63LryKF5XqKFKQ7MdFYGmE6jRDdboDeqQCeGXffTPw36TS1joiIiIhUK2ywJuarYdz9FqC409S2qVdCcy1FCNOdnq27nUDdzSBdhAC9a3eJ17e/1fR2IyVdvCsiIiLSb4ocrCcCL4e3NwFz0nhSHxlJ42k6RmG6+/olTPdyiBYRERERaUeRg3VqXw1jZguBhQCHTNlvTFjda7/92mhiNno9TEttnf6KrHa1E6p3btiRYktEJK80Q7uIiPSCIgfr1L4axt2HgWGA9/zRO8b8F5+XkN2vYXqfyfvmetS606PV7ejkaHUrgVpBWkRERNqlD+ukW4ocrJfT4a+GiYbbLIJ2vwZoaaxbo9VZh2oFahEREREpusIG6zx8NYxCsBRFq6PVWYZqBWoRkf6gEcRi0O9JpD2FDdagr4aR/tGN0epmQrUCtYiItEJhTkR6RaGDtUg/yHuobkYroXrrjl0ZtERERPqRgryIZKXlmbRFpDP2OWDfjr/m+MnjO/6aIiIiIiJFpRFryb08zww+fmowgV3Ws4Pvc8C+bY1cT5g6senrrMdPHp945Hpw8vhEp4MPTptQmFPBd+8aYefrbzW9XWmklEFrRERERCTPNGItkoLxU/fbE7Kzss8B+7Y1ej1h6kQmTJ3Y1DbjJ49PPHo9mHS9aROaaoOIiIiIFI97/E+vUrCO8JESb25+fc+PSDM6FbDb0Wy4huSnhitcSz/rtzcQIiIiMkrBuoHKkK2w3T37TO78dcbtKEK4bmX0OgmFaxER6Sf6UE1EQNdYtyRpuC5aGMyron6YkfX11+Vw3clrr5Ned92L11yLiIiIyFj6EGmUgnWG2g2E/RrMixqka6kcvc4iZLcbsCtHrpOE7PLIdaOAXR65bhSwFa5FikNvoESa0+hvRn9TIr1DwTrHei2Y91pgbkWWo9iVp4e3G7I7HbDLp4XHBez9JuzVsC0iIiIiIt2kYN3DFGTzq1OniUNrIbuZUey0A7ZGr0VE2qNRUBGRzlOwFumirE8Th+qJzpoN2klHsdMK2ArXIiLSDn2wICLdoGAdUdo9sifgZD2zs0ilToRsaH00u9mADfVDdtIJzrplZHeJN3bsano7L+kdnYiIiEi/UbCuo1a4UeCWrOU5ZKd5mnit0WuNWouIiIhIkShYtyCNoKNwLkllfT12WTshO0nAbnb0WuFaRERERIpCwbpL2glJCuX9Kfp778RIdpoBO8notcJ1+szsJuAI4EF3v7rOegcB97r7+zvWOJGM6Brb2lSb3qDfo0j+KFgXUBaBSmG9eOJ+Z2n3jU4H7Lxfd100ZnYqMM7d55nZzWY2092fj1lvMnAbMLHqSUREpCsUnkWKRcFagOxPM26GQn7rsgrbzZ4mnjRgN5pBHMLvuH4hQSMlzhBwT3j7EeA4oCpYAyPAmcD/qvdkZrYQWAgwY8aM1Bop0gqFDhGR3lTU47uCteROs0FQQby+tMN2M6PYjQJ2XLjut1FrM9sf+CdgHLCdIOCWgF+FPwB/6e4/rbH9nlO9gUOAWRWL5wM3hbc3AXPinsPdt4bPVbet7j4MDAPMnTu3oP/tFUtR31yIiIj0GwVrKbykIVEBfFQa12s3M4pdL2ArXPMJ4AZ3f9TMlgInAuuAb7n75+ttGD3VG/hK5aneZnYjMBjenQQMZLIHIiIiIn2u0MHazBYAhwMr3H11t9sj+VYvPPZ76B4/db+2R7GTjmAXJVzv3lVix+tvNr1dqeRjTpkODYejvVXcfUnF3QOBV4FjgJPN7IPAT4EL3X13zOZD1D/Ve3X42ErgaGBt0zskkpBG10VEpJ/lNlhHZ6itMbPtkcDXgIsI3kDGPc9E4E5gCvAicK67/vuXsZKEyl4P3+1+rVcW4bqoKk+ZjjKzZYw9Xfv77n6lmc0DJrv7SjMbAY5399+a2e3AScB9MU83EXg5vB13qvdy4CkzOxhYABxjZkcAH3f3RS3unoiIiIhE5DJYR2eorTOz7RPApcDtdZ7uHOAZd7/OzL4JzAV+VGtlHxlpGA4qT4FNKumsymlopX3SWKPA2SvBu53R62ZnEW+k26PWWXD3C6OPmdkU4BvAaeFD/+7u5eHyVcDMGk+3jTqnerv7VjMbAk4ArnP3LcAWIDZUu/tQ0v0QERERkVG5DNZUz1A7RMzpju6+iuBNZz0vA580s++6+wVpNK6TIbkVeW9fnF74MKCXTjXP+tTwfhi1TsrM9ga+DVzu7uX5z+8ws78FngX+HLimxuYNT/V2982MHj9FREREmqJzfZPJZbCOmaG20emO9Z7rfjMbBP7ZzJ4ALnX3kRSbKylo9sOAogXxWiE1z4G7W+E6ajD83use9imCY9oVZnYFsBS4EvhHwID73P2xGqdwLydyqndHWy4iIiIiQE6DdYy6pzvWY2Yzge8B3yG41vpsgtPMK9fZM9HQwZMGo08hOZQkiBchfOf59PK0vv+62XDdb6PW7r6UIExHHRVZ72dETuGucaq3iIiIiHRYUYJ1OzPbXgD8zN1vM7Nngarhr8qJht799sk62aFH9EL47nTwTiNMS2fpVG8RERGR7itKsF5O66c73gjcZWbnE0za87H0mydFVfTw3UwQbnfW707qt1FrERERESm2XAfr8gy17Zzu6O6vAB/Mon3SH7KYJb4b8hqok15rLSIiIiKSV7kO1pV0umO+pBGEJkydmEJLuq9e8C5K6M6jbo9aj+zaxZvbtzW9XWmklEFrRPqTZqIVEZGiKEywlvTkZXSwH8K5QncyGrUWERERyZY+rMyWgnVEaXcp8Rv8dkOdgkT7mqlh3kJ4P4Tudr9Tvduj1iIiIiIiSShYt0HBuFg69YFJGooeutsN1CIiIiIiRaJgLRLRKIB3O3jnNXRnFabHT676hjwRERERkVxRsBZpUr3gnefQXZZG+NaItIiIiIi0qhev91awFklR3ke7QaFYRERERCRtCtYiHVQreOchcHeT5isQERERkSJTsJbMdGM256Jej5vn08uzplAtIiIiIkWnYB1R2l2qGwiLGtyykMevQWqnTXn93fZy6M5zqC7tepO3Xt/U9HZe2p1Ba0REREQkzxSsm5R1cGvm+TsRBPMYnrPSaF/zGLyLcE13nDwHahERERGRZilYd1DaIbWfQm8eFPFMhmYCbFYhXCFaRERERHqdgrVICmqF7rwG7jgKwCIiIiIirVGwFslQXOAuUtgWEREREZHGFKwjSiPOzs1vMKjwIxkp4inlIiIiIiJSm4J1DTsbXL+s4C1ZSHrdvAK4iIiIiEh+KFi3qFHwrkWBXNKQJIArfAuAmd0EHAE86O5X11hnf+CfgHHAduBMd3+rc62UtLl3uwUiIiL9RcG6w1oN5I0UKbBnVYMkilSnduXtq9uS0mz36TGzU4Fx7j7PzG42s5nu/nzMqp8AbnD3R81sKXAicF9HGysiIiJSYArWPaKTI+jdDMbtarftvRrMFWar7X5rJ29sWd/0dqWR3Rm0pmVDwD3h7UeA44CqYO3uSyruHgi8GvdkZrYQWAgwY8aMNNspIiIiUmgK1hGl3SV2btix5/7gtAldbE32ihySu0HX3kuemdkyYFbFQ/OBm8Lbm4A5DbafB0x295Vxy919GBgGmDt3rk42FhEREQkVNlib2QLgcGCFu6/O6nUqQ3Y9vR7AJRkFb+kmd7+w8r6Z3QgMhncnAQO1tjWzKcA3gNMya6CIiIhIj8pdsI6bRAdYSvXkO0cCXwMuAmKDtZlNBO4EpgAvAue6ZzOlS9IADgrh/SzJGQIK35Ki1QSnf68EjgbWxq1kZnsD3wYud/cXOtc8ERERkd6Qu2BN9SQ6ZxE/+c4TwKXA7XWe6xzgGXe/zsy+CcwFfpT1DjSiEC71aMZ5SdFy4CkzOxhYABxjZkcAH3f3RRXrfYrgNPErzOwKYKm7393x1oqIiIgUVO6CdcwkOmcDi8P7eybfcfdVwKoGT/cy8Ekz+667X5Dk9UslZ+uOXQDsN2GvJlqeDZ2KLkklDeQK4P3D3bea2RBwAnCdu28BtgCLIustJTgzSERERERakLtgXVaeRAf4DUFAhgST71Ry9/vNbBD4ZzN7ArjU3UeSbl8O2I0UKYBnRcG+OLoxYZ3CfPe4+2ZGZwYXERERkQzkMlhHJtH5DAkn34l5npnA94DvEFxrfTZwW8x6e75CZtpA8yVpFMDzELyz1u1g3w36MCE5zT4vIiIiIr0sd8E6OomOmSWafKeGC4CfufttZvYsEDtsVvkVMu/aazD1yc3qBe9+CN29qtUPExTIRURERER6S+6CNdWT6NwCnFM5+U4Tz3UjcJeZnU9wXeHH0m5suzTa3X/aGd1XKO+c0q63ePP1zU1v5yOJrzYRERERkR6Ru2AdN4mOmd3H2Ml3kj7XK8AHm3n9XSVn/ZuN3xgfuM+4Zp62ZQreUimNU+4VzkV6QzZfHikiIiKtyF2wjpPHyXfyEr6TTrBWVg7izW7XCoX+fGo2nCuIi4iIiIjUV4hgXVRx4btTI921dCJQd+O1WqHgn0w/TkwnIiIiItIMBesOy2PY7lfdDv4K9iIiIiIivUHBOmKkC9esNTqtXMG7N+l0fCkLv2LwvcCP3X1Dt9sjIiIiIs1RsI6x6a3aQXfK3p0PubWCtwK3NNJqeFcg7xwzmww8ADwI3GBmH3L39WZ2E3AE8KC7X11n+0TriYiIiEh2FKyblKfQnfZId5IJ2dKiDwXyLY3RdIXzxI4CPuPuK8OQPcfMJgLj3H2emd1sZjPd/fnohmZ2apL1RERERCRbCtYpylPohs4G5WbluW1Z6bcPE7p9DXubRry0u7Utvbm+7e4rAMzsA8D7gCuBqxn9JoRHgOOAuMA8lHA9EREREcmQgnWH1Ard3Qjc0h1pfpjQbyG9C54tbV/P7jdexwaSHyZ95ybYayJmthBYWLFo2N2HAcxsGTCrYtn3gauAM4HNwC5gIvByuHwTMKfGSyZdT0REREQypGDdZQrc0opOjPj3c3h3993j3jEH3/xrbOrMxNuVNq5lYNph7N7y4jAwXOO5L6yx+afN7Crgo8A2YDB8fBIwUGObpOuJiIiISIYUrHMqLnArbEsntRveix7MS7/78Ttt4ttfHkgYrH1kF6Vtr8KG55o6rprZ54HfuvvtwAHAa8BqgtO6VwJHA2trbJ50PRERERHJkIJ1xG53Nu8aYfJe+QsF3Qrb9a4dl/r6+cOQVoJ5nsK4u78ycMAf4Ts2YhOmNl7/tV8xMPmPGXllY7M7PgzcY2YXAM8SXCu9L/CUmR0MLACOMbMjgI+7+6KKbZdH12vytUVEREQkBQrWNWzeNfreOI8hu6zVU8kVljsjzTr3Q0jP26R2vuWFj5Tets8j4yb8p/rruVPa+Dy+Y/0fNP0a7puBEyIPbzWzofDx69x9C7AFWBTZNm49EREREekwBesEKkM25Dtolyk4955u/k77IdTX8HhpxwYGRt7Cxu1dcyXfuQH2noRvf/X3ab1wGLjvSWs9EREREcmOgnULihi0RdrRTqgvcih399K4d/5HSpt/xbhph9Vcr7RhLf7ar4c61zIRERERyRMF6xREg3aZAnf+1frdVdLvsT1JQnmew3fplVUH2oRp633qLMysarnvfjP4mi34QccbJyIiIiK5oGCdoXqhTWEtkCTYdls329gv/STPly64+4aBKf8B3/EqNvGgquWlzb9kYMq7GFm3wbvQvNSY2RTgvcCP3X1Dt9sjIiIiUiQK1l3Sb6PcRQjQedTpuvVq/2uXb/7lcSX44UAkWLs7vukX+M6NU7rUtIbM7CbgCOBBd7+6xjqTgQeAB4EbzOxD7r6+g80UERGRLvJCDw/kg4J1zhQ9cCtAF1sWv7+i9N0GnubNLfjuN7C3jd/zoG//PQxOwXds2NzFttVkZqcC49x9npndbGYz3f35mFWPAj7j7ivDkD0H+N8dbayIiIhIgSlYF0SWE6YpDEuWeuE6dnf3cYfMo7TpF4x7+5F7Hi9teA5/7dd/1sWmNTLE6IzhjwDHAVXB2t1XAJjZB4D3AVd2qH0iIiIiPUHBOqLU7QYk1G7QVpiGnSPpnPMyOK56QitpThHmIyitW7k/g1O2DBz4p5gZvmsnvLUN4EfdbluZmS0DZlU8NB+4Kby9iWAkuta2BpwJbAZ21VhnIbAQYMaMGSm0WERERKQ35DJYJ5lEx8wWAIcDK9x9dZqvXw5cRQpMCsr1pRWiO/3cWSpK/85L33b3rQPTZuHbfovtezClTc8zMHUmu7e/mpsO4O4XVt43sxuBwfDuJGCgzrYOfNrMrgI+Ctwds84wMAwwd+7c3Oy3iIiISLflLljHTaIDXEv15DtHAl8DLgJig7WZTQTuBKYALwLnhm8eEyliwO4lRQ2sRdFMffU3EPCNP59T2v3Wv9mkd1Da/Gt4Y9O+3W5TA6sJTv9eCRwNrI1bycw+D/zW3W8HDgBe61D7RERERHpC7oI11ZPofIj4yXeeAC4Fbq/zXOcAz7j7dWb2TWAuLZy2qYDdGQrS+ZXF76aIf0/u/uOBSQfhm55nYNLbGdm5cVu329TAcuApMzsYWAAcY2ZHAB9390UV6w0D95jZBcCzBNdji4iIiEhCuQvWMZPoTCFm8h13XwWsavB0LwOfNLPvuvsF7batMlwUMRTkkcJ0/0ryu8/j39nAlEMZWfcM+MhR3W5LI+6+1cyGgBOA69x9C7AFWBRZb3O4joiIiIi0wJo4M7pjwkl0/icwHdgAfN3df2JmHwHmuPu1TTzXGcCXCEe43b3qgs3KCXmAaeFrdtL+BG92O6nT+9mNfeyHukJ/1Ha8ux/ZeLXsmdmEgXe+b/vIun/NX+rvIDNbD7zQ7XYk1I2/y6JQbWpTbWpTbWpTbWpTbWpTbWrLW23+0N0PjFuQy2BdFk6iczpwfnhq+KnAYe5+TcLtZwK/B7YTXGv9PXe/LbMGt8jMht19YeM1U33NVe4+t4Ov14197Pm6hq/Z87XtRl2ld6j/1Kba1Kba1Kba1Kba1Kba1Kba1Fak2tScIbZbzOzzZnZuePcAgonLjgvvHw38pomnuwD4r+Eo9bPA+JSambb7u92ADujGPvZDXUG1FRERERHpqtxdY031JDrLgR9UTr7TxHPdCNxlZucTnLb6sZTbmgp37/mQ0o197Ie6gmorIiIiItJtuQvWcZPoxEy+k/S5XgE+mGb7eshwtxvQo1TXbKiu0g71n9pUm9pUm9pUm9pUm9pUm9pUm9oKU5tcX2MtIiIiIiIikne5u8ZaREREREREpEhydyq4dIaZLQAOB1a4++put6eXqLbpUS1FREREpAg0Yl1wZnaQmT0V3n6bmb1oZk+GP+8OH7/JzJ4xs0UVmx4JfB04ts5zrzWzKWa20cwONrMnMt2ZnInUdn8ze9jMHjGz75rZ3uHjqm0LzOyPzexBM3vKzL4aPqZaSksif6txfauZY2NPaVSbivWWmNkpFff7vjZmdlFFn1ljZsvCx1Ubs8lm9pCZrSrXJXxctanxd9brtYl7nxS3z0kf6xVN1GVPv6p4rGfrAslqE7dO+Hgua6NgXWBmNhm4DZgYPnQU8C13Hwp/fmrBd3+Pc/d5wJ9Y8N3eAE8AlwL31HmJDcB7gf2BPwVeyGI/8iimtp8AbnD3jwC/A05Ubdvyd8BV7v5+YLpqKa2K+VuN9q0hmjs29oyEtcHM3g/8QXm2f9UmqI27Ly33GeAp4B9Umz395hzgrvC7Zfc1s7mqzZ7aVD3WD7Wh+n3SWUT2Oa4OfVCbJHWJ9qu+OA6ToDYx69R7/911CtbFNgKcCWwN7x8DnGxm/yf8JOdtwBCjoeQRwu8Ed/dV7v4/3P33dZ7/BWA+8MPw334KLGNq6+5L3P3RcNmBwKuotu04FPi38ParwFdRLaU10eNgtG/tTxPHxh7TsDZmthfwD8BvzOy/hMuGUG32L69oZu8EDnL3Vag25dpsBI40swOAQ4CXUG3KtYl7bIger03M+6Szqd7noYSP9YyEdYn2K+jxukCy2jTz/jsPFKwLzN23Rr5+7EfA8e7+PmAv4CSCT79eDpdvAg5q4iV+A3yAoNN+ILzfF2JqC4CZzQMmu/tKVNt23Av8jQWnnp4IfB/VUloQ87ca7VuPk/6xsRAS1uZc4GfAdcD7zOwvUW3KtSn7NLA0vK3aBLX5IfCHwF8B/4+gFqpNUJu4x3q+NmXl90kEH7ZE9zmuDn1Rm3p1qfGesy/qAg37zJh1Unj/nSkF697y7+7+2/D2KmAmsA0YDB+bRHO/8xcIRnoeJbjGta9HAs1sCvAN4L+FD6m2LXL3q4GHgQsITn9SLSUV0b7l7ttI/9hYSDVq8x5g2N1/B9wJfBDVplwbzGyAoCZPhquqNkFt/gb47+5+JfAccD6qzW3uvq1GvfqiNpH3SXH7nPSxnpKgLnF6vi6QrDYpv//OVG4aIqm4w8yONrNxwJ8DPwFWM3qKxNE0N5r3G4JTLv4vMK7JbXtKOFnCt4HL3b0c3FTb9qwBZgA3oFpKutYw2rcg/WNjka1hbG1+AfxJeHsuwQdTqs2o9wP/6u4e3ldtApOBd4d/U38GOKrNDXUe6/naxLxPitvnpI/1jIR1idPTdYFktcng/Xem9HVbveVK4B8BA+5z98fMbD/gKTM7GFhAMLKX1AvAL919h5mtIzhFo199CpgDXGFmVxCcFrgc1bYdnyOYkGKHmS1HtZT07Olb4f20j41FFq3NTcDNZnYWwWnypwOvo9qU/WfgBxX3l6PaAHwFuIXgdPBngG8RDNaoNvGPLaf3axN9n3QLcE5kn53qOsQ91kuS1CXOcnq7LpCsNmm//86UjX4IK70qnG3wBOAH4el+khLVNj2qpXSa+lxtqk1tqk1tqk1t/VibuH1O+lgvS7q//VYXKH5tFKxFRERERERE2qBrrEVERERERETaoGAtIiIiIiIi0gYFaxEREREREZE2KFiLiIiIiIiItEHBWkRERERERKQNCtYiIiIiIiIibfj/HOZO2yTkqb0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1440x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 绘图部分\n",
    "fig =plt.figure(figsize=(20,6))\n",
    "# SVD空间场\n",
    "ax1=fig.add_axes([0.1,0.55,0.48,0.4],projection=ccrs.PlateCarree(central_longitude=105))\n",
    "ax3=fig.add_axes([0.1,0.05,0.5,0.4],projection=ccrs.PlateCarree(central_longitude=240))\n",
    "createmap(ax1,70,125,15,55)\n",
    "createmap(ax3,190,270,-30,30)\n",
    "# 设置标题\n",
    "ax1.set_title('(a) 我国气温svd(1948-2021)',loc='left')\n",
    "ax1.set_title(str(round(frac[0].data*100,2))+'%',loc='right')\n",
    "ax3.set_title('(c) 中东太平洋海温svd(1948-2021)',loc='left')\n",
    "ax3.set_title(str(round(frac[0].data*100,2))+'%',loc='right')\n",
    "# chinasvd\n",
    "ax1.contourf(lon,lat,lp[0,:,:],cmap=plt.cm.RdBu_r,zorder=0,extend='both',levels=np.arange(-30,11,1),transform=ccrs.PlateCarree())\n",
    "# ZDsvd\n",
    "colorbar=ax3.contourf(lon1,lat1,rp[0,:,:],cmap=plt.cm.RdBu_r,zorder=0,extend='both',levels=np.arange(-30,11,1),transform=ccrs.PlateCarree())\n",
    "colorbarax=fig.add_axes([0.45,0.05,0.01,0.9])\n",
    "plt.colorbar(colorbar,cax=colorbarax,format='%.1f',shrink=0.5)\n",
    "#SVD时间序列\n",
    "ax2=fig.add_axes([0.5,0.55,0.4,0.4])\n",
    "ax4=fig.add_axes([0.5,0.05,0.4,0.4])\n",
    "ax2.set_title('(b) 我国气温序列',loc='left')\n",
    "ax4.set_title('(d) 中东太平洋海温时间序列',loc='left')\n",
    "###\n",
    "le1,year=smoothsvd(le)\n",
    "bar=ax2.bar(year,le1,color='black')\n",
    "for i in bar:\n",
    "    if i.get_height()>0:\n",
    "        i.set_facecolor('red')\n",
    "    else:\n",
    "        i.set_facecolor('blue')\n",
    "ax2.axhline(y=0,linestyle='--',color='black')\n",
    "#-----------------------------------------------------------------------------------#\n",
    "re1,year=smoothsvd(re)\n",
    "bar=ax4.bar(year,re1,color='black')\n",
    "for i in bar:\n",
    "    if i.get_height()>0:\n",
    "        i.set_facecolor('red')\n",
    "    else:\n",
    "        i.set_facecolor('blue')\n",
    "ax4.axhline(y=0,linestyle='--',color='black')\n",
    "##保存图片\n",
    "plt.savefig('../../picture/Pythonhome/12/ex5.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#----------------------------印度洋海温和我国气温的svd分析-----------------------#\n",
    "YDsvd=xMCA(airchina,YDsst)\n",
    "YDsvd.solver()\n",
    "lp,rp=YDsvd.patterns(n=1)\n",
    "leYD,reYD=YDsvd.expansionCoefs(n=1)\n",
    "frac=YDsvd.covFracs(n=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lon=lp['lon'].data\n",
    "lat=lp['lat'].data\n",
    "lon1=rp['lon'].data\n",
    "lat1=rp['lat'].data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QpaQCx9EL9lrGojhzxgJMiOIUvWgLfp75haoenpKDRTCQJiWj+AcRaaiqqyu6PhWlUaNG2qZNm4quhjHGGGNMykydOnWlqjYOti5SsJ7lN4jKFCBfVZf7/d4+yrqsAOYCBcDYYAXcEWWHA3Ttvpd+Mua7KA+R2fJzqmaq3rw9PRLpxm3xjwm0sShh4yaltdWbvUxFnBiHd2rSKGUHi+AX1rMLNWhM3iqq8LzCbdq0YcoULx2VjDHGGGMqBxEJ2WMx0j3Wr4hINxHJBo4Frg/4fWa4jUN4BOgbw3aVWn6OVNlQnU5q5mZRMzfSn0WEfeRlJ6g2Jt2ISONCttOHBixgo69buDHGGGOMqeIitVjfAbyO0yrzMfAf/99V9etoD6iq00VkXLTbVVYWptOntdpfzdysuFqva+ZlV+qW61S2VqeTfaj3Tx5ZVCOLRuSxL/VLqMKt1sYYY4wxxhE2WKvqHJyRwf0F/u6Jqp7t93NBLPuoTCxQpz9fy3WsAbuyhuuqGqpFJKshuQykKQBdqMUMCiu4VsYYY4wxJh3E1+fVxMRC9Q7p2FodKJ7u4TXzsitV1/CqGqoBjmCn4iZUI8/92GxMNTZSjIg0reCqGWOMMcaYChapK7hJoGQF6ny/0Lc5AYNvmeDi6R5eWVuvq5JfWE9P6pVZ1omatCJ/OdYd3BhjjDGmSrMW6xSJJ1Tn52aFfQQrmwkyobU6UDwDm2V6y3VVbq0WkZ23UEJD8sos35Wa/OYMYpbZL64xxhhjjImLtVinQCyhOt5wbK3YyVMVW64TGaob5Ocmbd/J0oO6S+sE+bjMJYvmVKcX9bZjrdbGZIZQg/lr5n3Za4wxJn1YsE6iigjU4faZTgE7E1ur/VXFcB2LwBAdrky6BmwRyWlALscR/FbqztTiR9amtlLGGGOMMSatZEaf4QyULqE6cP+Z0k08E1SVbuHRBt4G+bmlj1i2Szf9abxtZ6qTE+LjsgF5bKMEEWmd4qoZY4wxxpg0YSmrikmXVmsbGT0zVEQrcrqF619ZT2dqhy3TmdrsSZ0/UlMjY4wxxhiTbixYp5FEh97N20rKPdJJVQ7X6d4VfPXmbTGH6nTt0h0LEdm1BKhH+LDflhosZjMikl7fChhjjDHGmJSwe6zTzOZtJTF310634OxFfo5k/P3W0UrnUF2ZQnEidKPOwsYBI4EHk43Qknx6ULcIG8TMGGOMMabKsRbrDJcurdHx3L+dqS3XsQxelq6hOp4W6lD7y3QiUm0pm2lDDU/lO1OLX9iQ5FoZY4wxxph0ZME6SeJphY0UktMlTAdjg6NllkQH6kRJh/usD6bRltbkk+2xAbouuWQBItIhUlkRaSIi4/1+HyEiE0VkWITtPJUzxhhjjDGpZSkohFinUkqUwNCcSWE6lnCdqa3W0Ui31upkB+p0DOzR8DJoWaDO1KYrteeFKyMi9YGXgJru78cD2araG2grIu1DbOepnDHGGGOMSb0qH6w3bisJ+vCtq0jpHKZ9QoXoyh6uo31vpFOoTmUrdaaGaxFpm4NQO8phKFqTz1K2RCpWDAwGCt3fC4C33Z+/BPqE2M5rOWOMMcYYk2JVcvCyaELRxm0lcc1XXJXl52ZF/aVAZRzMLN1CtfGkfl3JIT8r2i97hNxiQUSGAEP8VgxX1eEAqloIIFK675rAX+7Pq4G9QuzcazljjDHGGJNiVSpYx9oCXRnCdbCwGm8LsZdW6XQN16kK8BaqnePGcs90g/zcjP0iwA3Rwz0W3wDkuz/XInRPIq/ljDHGGGNMilWZC7OK7tadapu3a5lHqDKpkG7dwuPdt9f3UrqE6nQdoMyUmsqObt3dgD/iLGeMMcYYY1Ks0rdYJypQZ0KrdSxB2bdNtGEz2rAcS8t1JkunUJ0OYm21riI+BMaLSHNgALCviHQBTlXVYeHKpbqixhhjjDEmuLRMiiJytIgMLyxcF9d+qlIrdbytz9FuH21ITqdQHa4VPxIv7ykL1WU1yM+1UB2Eqha4/xbiDEw2CThIVdep6i8BoTpouZRW2BhjjDHGhJSWwVpVP1HVIXXq1I15H4kO1eneWl0RI2p7DcuxhOp0HMDMQnV0LFB7p6prVPVtVV2RiHLGmAwiEvxhjDEmo1TKruDp0FKdSVNH+WzerlHXe/O2kpDdwmNtpbZQHZ+KDtUWpo0xxhhjTFVT6YJ1MkJ1urdW+1TUVFXBwnU6df2OV6T3VDoE6ooO02CB2hhjjDHGVF2ZkRg9SoeW6kwXazD3BenN20riCtXp1lqdzFAd62jdvu38HxXJunynHxEZISITRWRYmDJNRGR8tNsZY4wxxpjyKl2LdaLF0lqdid3A/cXSJRzib6VOp1CdrK7fwUJwRQfjeFTmQF0tS6ifmx31dtkV/P2eiBwPZKtqbxF5QUTaq+qCgDL1gZeAmtFsZ4wxxhhjgsv4FuuN20pKHybzQ306SEaoToeW5URKVSt1ZQ7uSVQAvO3+/CU75r72VwwMBgqj2U5EhojIFBGZ8u+//yaqvsYYY4wxGS9jg3UqwnSm3FudDKluPU6X1upEh2oL1KYC1AT+cn9eDTQJLKCqhUGm6/Ky3XBV7amqPRs3bpzAKhtjjDHGZLaM6wqeqpbpWEN1OrQYV9QgZrFKl7omKlRXpiDtY2E6o2wA8t2fa+H9C9RYtzPGGGOMqfIy5sIpld2906GlOj83q/RRUdIl8Cab1/dWVQzV1kKdkaayoxt3N+CPJG9njDHGGFPlZUyLdc3crIQH60QH6HhbqxMZohPVah3rQGbR7L8ieX1PVbVQnU5hOp3qkiE+BMaLSHNgAHCyiNylqpFG+g7cbt+k1tIYY4wxphLJmGAN6dGS7C+ewBlNiM7PzarQeaGTFa4tVKcXC7CVg6oWikgBcChwv6quAGaGKFsQZrvAe7CNMcYYY0wIGRWsK1qqgnSiJPJea99+EhGwUx2oY+3pEM8c1ZnCwnTlpKpr2DHCd9K3M8YYY4yp6ixYh5GIEJmoQF3RrdY+sQbsVITpRN4qUJlDtYVpb6plZdEgL/p5rHOKKn4AQ2OMqRQkyOepVo3xX4wxmceCtSuRXZ0rcsCxQMkaIdxr9/BEHTuV85RXxlBtYdoYYyqZYKETLHgaY0wFqXLBOlkDcaUiTMfaap3McO3bf6h18UploIbKFaotTBtjjDHGGJMaVSJYV9bW6HSRjNCe6kANlSdUV1SgrunXbbqynEtjjPHMui0bY0yVlpBgLSIDgM7AOFWdmoh9xiud7o9OpHRrtU60igjUkNlBMNVBuqaH+44tZBtjjDHGmKrEU7AWkSbA58DewCL3AXCZqs4GdgceAYYCQYO1iIwElqnqTSJyG4Cq3hZH3ctJp3mkTfQsVHuXqjDtJURHs49MPNfGGGOMMcZE4rXF+kEgH+gKvKGq1wes/xa4Eng5wn4uEJE7oqtieJk2BVYiVLZW64oK1JA5QS8dW6Xj2W+mnHdjjDHGGGO8iBisReRgYCOwAtgXOEpEDgJmAxeq6nZVnQJM8XC8OcBpcdQXSJ8wnVdSFPU2RVl5CTt+prMW6tDS4T7pVB0nE14PY4wxxhhjwgkbrEUkD/gvcBzwIfAT0E9Vl4vIy8ARwMdRHO9J4DpgdJhjDgGGALTYuWXa3SsdS5gOtn28AbuytVonS6pCW2UYgTtVoTrScS1oG2OMMcaYTBOpxfoG4ClVXSvOaJezVHWru24K0D7K460A5gIFwNhgBVR1ODAcoPuee8WcANMpTPskqrU6lkANyRm9Ox413dcomS3X1jIaWUUF6lDSpT7Vs4XG1aKvS86m5EzpZ0xC2QjWxhhjTEJFSp/9gEtEZCzQHfhMRLqJSDZwLDAzhmM+AvSNYbuI8nOzSh+JkFdSFFeoLsrKK/NIhMoSqv3VzM0qDdlJPU5edunDYOfCmEwgEvxhjDHGmLQStsVaVQ/0/eyG6/8ArwMCfKyqX0d7QFWdLiLjot0umEQPPhZvy3Qy75+ONVBDeodqf6lowS49VhVuybYwbYwxxhhjTGJ5nsdaVQvcH7vGciBVPTvIvjxJ1ujdieriDckL1VUhUAdKZcCGqjFStYVpY4wxxhhjksdzsK4IWZKe90qnQjyBGjI3VPuzgB0/C9TGGGOMMcYkX1oH61ilOkAnsrU6EwN14Mjtia5DzdyslE7NlekB28K0McYYY4wxqZXxwTqTWqHDiTdQQ2pCtZfpz5IRtFPdeg2JuQ/bQm78UjGwnTEVKtRgZDZKtzHGGJMxMiZYp2uATkRrdTrfR52QecT99hFvfSsiYIMF5FSzMG2MMcYYYzJJWgdrQdM2UCdCOgdqSEyoDrfPeJ5DRQVskzwWpo0xxhhjTKZK62Cd7mJtrU73QA3JCdWhjmEBO735B95En+d0DtN5OVnUqZEb9XbZ62yOYWOMMcaYqsaCdYrFey91IsJo4L4qWiJasQMDmgXt6HgNuOkchI0xKRDsfvDKdC+43e9ujDEmRhasUyw/NyshA5WlSyhOtEQNfGZBO7hMDMbV7fZ2Y4wxxhiT5ipFsJaijQBoXs0Krok3vrm5ExGwK7tE35PtryLDdjK7VwfuP1P5XvuH7ru3gmtijDHGGGNMeBkbrH1hOnBZpoRrsIAdrUSOLg7hw2e8YTeaYFsZQnCiBPZYGP3Jx7z5+msVVBtjjDFpy7rtG2PSTEZd0UvRxtJHpDKZJD83qzRkG2/yc6T0kQw1c7Piehjvwr2Ws2fN5KSTT6mAWmU2ERkhIhNFZJjXMiKSIyJLRGSs+9gjdTU2xhhjjMlsad9iHWtIzrTu4UCZcJ3oVux0Cu4Jf24Jui/bpEY0X4bUrlOHf/7+O4m1qXxE5HggW1V7i8gLItJeVRdEKgPUBt5Q1esrot7GGGNSwFr6jUma9A7WJfEHsEwM2BC8m3g6heN4hHoeiQrcFrRjt2r5n7z60ossX76ce+5/kJo1Q//deDmv8fQoKCkpoXDNajZvXB/zPtKRiOQAi9wHwGWqOjtIuRFAF2CUqt4VxSEKgLfdn78E+gALPJTJB44SkYOA2cCFqro9oE5DgCEArVq1iqJKxqSYhQdjjDEpVjmSmgeZ2EUcdnQTryyhOhz/55rI5+zf1biyjqbuVeC5yM8RqmfDD998yfmnncihffdn/fr1bNtWxOknDWLjxtB/M8H2lahzPfqj99mvZ3fGfvsNp552esz7SVNdcVqGC9xHsFBd2qIMtHVblL2qCfzl/rwaaOKxzE9AP1XtBeQCRwRupKrDVbWnqvZs3LhxFFUyxhhjjKnc0rvFOgkybYCzqi4wXCdjqrKKbNFORNAPrH80+/x90W/ccM1VLF++jIsvvZznXnyZGjVqUFJSwpWXXcLpJw3ivU9Gk5WV/C928nOzUFX+9/jjPPXk/3hh5Evs27s3EqrlKcmyq+eQ36hG1NvJv1llWnZdw1V1uPvzvkRoGcZbq3MoG3BanwFqEfwL1GBlZqnqVnfZFCCaMG+MMcYYU6VVuWANFq4zWSqCNiQ2bCe7lTyW/W/dupX/Pfowzz/7NP+58hrOv2goubm5pevXrl3DH4t+o1EKWiXzc7NYt24d4yZO4JWXXuLPpUv54usxGd3V2A3Rw0Os9rUMLxeRl3Fahj8OKBPYorxXFIefihPEJwHdgHkey7wiIncDc4BjgXuiOKYxxhhjTJVWJYN1VZFXUpSwfRVl5SVsX4mUjKANyQ/DFen778Zx3VVX0KFjR74a9wM7t2xZZv3CBfM5ffCJHHn0QG6+9faQrdXFxcUsmD+P+fPmUnDQIdSpWzfqusyZMYWbb7yR2bNm0aNHTw46+GBGvDiS6tWrA4l9D6cRLy3DXlqdQ/kQGC8izYEBwMkicpeqDgtTZl9gFvA6IMDHqvp1FMc0lUmwXiLx3pucjHueK+I+6mScG2OMMZVCWgZrETkaOHrXXdok7xgJGNQs2EV/RQXQZAcQ//2na8iG5I6sXhmM/uRjrrv6Ch567H8cNqDcLbTMnzeX444awI3/vZXTzzy7zLq//vyTnyZPYsa0acyYNpXZs2ex005NaNmqFTdffy3XXH8jRxw9kMaNdwpbB99r9OAD9/Ps009z5933cNzxx1OtWrUy5SppqAZvLcNeWp2DUtVCESkADgXuV9UVwMwIZdYB63Du/zbGGGOMMVFKy2Ctqp8An/To3u2CZB8r0aOGxxoGwoXVdAsYvvqkc8CG5LVmZ6pvvv6Ka678D2+++yFdu3cPWqZGjZoUby/m0P6Hly7btGkTD9x7N2++9gr79N6P7nv14Mprr6fbnntSr159AGbPnMl999zFPXfcTl61PM45fwhXXevM2hRqELqPPviA1958i169epVbl27v+QS7A7+WYWCZxxZlz1R1DTvu0Y65jDHGGGOM8SYtg3VFqOhpuTIxSGRKK7ZPVWrN/m3hAhbMn8/GjRvZuGE9q1at4rmnn+Sl198KGaoBdmrShF3a7sJH777Ff664gjFff81VV1zO3r324aep02i8U/DW6F499+TVt95BVZk0fiyXDL2Im2+6MeRxVq1axcyZM2nWrFm5dZn4txANVZ1D+ZbhYQFlgrUoG2OMMcaYNGXBOkBFB+xMFRiG0j1oV+bW7FEff8S1V/6HvXr2omatmtSqVYuaNWvx0utvsfc+Oxo+A8/BunXrOOPUU/nl559p2rQp/3vicWrXqcODDz9C/8MOi3hc3/4+/fQTTj7llLBl//rzTwDOOetM7rr7Hvbt3Ruo/KE6GtaibIwxxhiTOSxYh+A/57WF7OiFCkjpGriDdVdOZNhOxJzcXurz6ssjuf+eu3jr/Y/Zo1u3qOpw0w3XM3/+PI459jgOPPBA/u/+B2jTpk1Uddy4cSOvv/oqM2fPCVuua7durF5XyIcffMBpp5zCdVdfwaUXDw09GJExxhhjjDFpzIK1B/4hOxwL4JFl0kjliQjDieQl/L/43HAuvPjS0lAdzXN4/H9PkpWVFde80WO+/orNmzezbt06mjRtGrZsTk4OJ5x4Ir17dOOU089k1uw5DH/6yQqbt9oYk4YqYuRvY4wxJgYWrBPIawCPV6QAX1W+CAgX0tO1ZTzRAoPziBde4IgBh9O/3yF0DWixjiQ7OzuuuiyYP5//XHYZH386ig4dO3raJq+kiF3atOHbr76goF9/nn/hRS4479y46pEoOdWyya9fPertsrLti4Eqy0Kgqczs/W2MMWGlV5Oc8USKNoZ9xLKfyiavpKjMo6ro3KUL9z/wIKefdipTp05h69atkTdKkGuvuYaLL7mEvgUFEcsGvi75+fm88uIL3H7n3fz8yy9lylbG96cxxhhjjKlcLFgboHKHbCgftCtz2D7xpJM477zzuXToxbRq0ZwD99+PB+6/L2T54uJi3nzjDX788ce4jrvf/vvx1JNPcsV/LmPVypVcfdWV3HDddSz8eaanc9+hQ3vuvftOTj/rXDZt2gRYqDYmo4iUfxhjjDFVhAVrU06sLeCZpjIH7cuvvJKJkyfz+5Kl3PzfW3ju2WeDlpszezb9Dj6I54Y/ywXnncvBBX159513kK0bg34ZEe4x7Nqr+PH7cSxd/Af77t2TST98T+0a1Tly4HHcc9/9nup9+qmn0K1bV665/sZK/d4zJiGCBVkLs8ZUHvb3bUxGsWBtIqoqASdVQTuVYb5GjRr0LShg9erVvPvOO2zevBmAzZs3c/utt3D0UUdy1tln89WYb5g+cxbXXXk5Lz4/nPadd+fAg/px2JFHc/2NN3s+Xsudd+bJxx/l1ZdeZMJ3Y7n9lmFM/H4cI158iU8+HRVxexHhiUceYtx34/ho1OcxP29jqjwL3cYYY0xK2eBlxoTgNfgGDpQWTWBOxfzf1atX58WXXuL5557nqiuv4Oijj2bChAnsscceTJz0I02bNSuty8Cjj2Lg0Uex6Pc/+Pfff9m0eRNnnXM+F114Abt4nHqrVcuWtGrZsvT3pk2a8MYrLzHopJPp0L49HTt2CLt9nWpZ/N/tt/DI/57mmCMPj/l5G2OMMcYYkyqVosU6a8v6qB/Gu0wfPTzZEtnSnaxW86MHHsNHn3zCxB9/pFOnztxz7728/OprNG3WLOix2u7Shl5796TVzi3p0KE9o0Z/Ftfxe+3dk1uG3cQJJ59KYWFhyHK+3hEDDj2YRX8sZt6ChXEd1xhjjDGmUrEeSWkro1us4wnIobYtqV475n1WRhaqK5Z/4E1Ea3aLFjtz2eWXB93/qlWr+fqbb5g+fQbTZsxg5qxZ1K5dh726d2P33XeL+ZhPPv0M77z3AbNmz6Z9u135++9/qFOnTrly/rcc5ObmMnjQsbz9/kf89/qrYz62McaYKsimBjPGVICMDNbJbHEOtu+qGrYzKVSHuw88k55HOL4QnKju4v6heu3atRxy2ADa7tKGfffZh2uvvoruXbvSuHGjuI8z/vsf6N6tK59++B61atUKWibY69e6ZUsW/LYo7uMbY9KchaDKIdjraK+hMaYKyahgXVFduLO2rK9y4TpTwqiXgdUqW+iOJ2AH615eVFTE4NPOoN8hB/Hgff8Xd/0CXTjkfK6+9gZq1gx+rkO9PiJCSUlJwuvjVXZeNtXrV496u6zsSnGHTdVmAcEYY4wxUcqYK8CKvi+6oo+fSpkQNhM1FVgmTysW7SBpwcr/OPknjhh4LHVq1+a+e+5OZPVKFRx4IKrKuPHjy60Ld96zsgS1MGOMMdGzezBDs3NjjEmStA/W6TTYWDrVJRk0r2bah+pkhuBMnL873CBn4QZBmzFzJsedMJjTzjqHU08+mddfeYns7Oyk1FFEGHrhBTz9zHDnd4/nOCsrixKtuBZrY4xJmEwJchY6jTEmZp6CtYg0EJFDRSTkDZciMkBErhKRHgmrnRYnbFeJVBnDdVUO1JGOmQmB2z9Ahwvbqsqll1/JcScMpv+hh/DzjKmce/aZ5ObmJrxO/ufshKP689kXX8LWDZ63X716DdlZyQn7xhhjjDHGJFLEe6xFpD7wKTAKeFhEDgamAr5RhS5T1dnA7sAjwFB3fbB9jQSWqepNInIbgKreFt9TqBi+cF0Z7r1O51CdjkE2kXVK9LmP1D38uREvMGXqVObMmBrynudoeD0Xv8ydT9fduiAeWz5+X7yEJ559ni8/eiee6hljjDHGGJMSXgYv6wpcpaqT3JB9LvCGql4fUO5b4Erg5Qj7u0BE7oi+qukp0wc2s1BdsXzPMRWvw+w5c7jjrnv49qsvogrViXgdvvthIgf26R2x3LrCQqZOn8nt9z7A1f+5hN06d4r72MYYY4wxxiRbxGCtquMARORAoBfwLnCUiBwEzAYuVNXtqjoFmOLhmHOA00KtFJEhwBCAVi2ae9gdyNboLvy1WmJDTKaGawvV6SMVAfuCiy7mpBMH0a7drp63SdTr8OOUaZx3xqlB182dv5DHnnqWHyZN5s9ly9iz6x4cUnAglw+9ICHHNsaYjGVTkRljTMbweo+1AIOBNcB0oJ+q9gJygSOiPOaTwIWhVqrqcFXtqao9GzWsH+WuvZGtG6MO45VRVQuvmSCZr8nNN1zPN9+O46BDD+ebseMijridyLo0b9qEFf/8U2bZ74uXcP4lV3DIUcfRuuXOvPr8U/yz6FfGfPo+t9xwTdIGUzOViA20ZIwxxpg04Wkea3WuwC8RkTuB5qrqmzdnCtA+ymOuAOYCBcDYKLctJ56A7Ns20S3YmUSKNqZdy3VVD/zJek2OPupIjhhwOG+/+x7/ueIqmjVryv333s2e3bsHrUMi9dizGy++8gazf/6VFX//w9///MPCRb8z9Pxz+Pmn76lXt27Q7SpyoMCc6jnUaBj965CVk/aTLRhjjDHGmATzMnjZ9cByVX0ZqAc8IyJzcbp0HwvcE8NxHwGmkYBgnQiydaOF6zQJ14kIdF7DWDp3309W1/Ds7GxOGXwSJw46nudGvMDpZ5/LnOlTywwqlowvNo4ecBh///MvjRo2oF/BgTRtuhMd27ejfr165cpWxlH3jTEmbVj3cmOMSQovLdbDgbdF5HycMH0g8BogwMeq+nW0B1XV6SIyLtrtAiWyO3e8rdeZep+1TyoH0UqGWMJYJrxmyfrSIycnh4uGXMDzL4xk3PjxFBx4YOnxkqFpk50Ydt1VIddbmE4sERkBdAFGqepdXst42c4YY4wxxpTnZfCyNcChAYu7xnIwVT3b7+eCWPbhk6x7pK31uuJaryuiC3hVDtciwnnnnMWIF1+i4MADU37+LUwnh4gcD2Sram8ReUFE2qvqgkhlgD0ibedv3rx5FBQUlFl20kkncfHFF7Np0yaOOKL88Btnn302Z599NitXruSEE04ot37o0KEMHjyYpUuXcsYZZ5Rbf/XVV3P00Uczb948Lryw/FAdw4B+wIwZM7jiiivKrb/nnnvYb7/9mDBhAjfddFO59Y8++ijdu3fna6DctwoFBTz77LN07NiRTz75hIceeqjc9q8ALYG3gKcDtgV49913adSoESNHjmTkyJHlth8N1ACeAt72285nrPvvg8CnAevygc/cn++8807GBOy7IfCe+/ONN97IxID1OwOvuj9fAcwIWN9hyBCGDx8OOKOLzvdfWVBA9+7dedT99XTgT791AL2Be91Fg4BVAesPAf7rLhoAbPZbB3AUcI1vk4B1ACcBFwObgCMC1gGc7T5WrlxJ+XeeM0/oYGApUP6dB1d/8onz3iPIIDEFBQwbNsx57+GcP/914HTt2w+YANwUsA7gUaA7lH3v+a1/FugIfAI8FLAOAt57QZ7/u0AjcN57QZ5fufdegLHuvw/izL/qL3/AAD77zHn33Qll33sFBTRs2JD33nPefTfeeCMTJ5Z99+288868+qrz7rviiiuYMWNGmfUdcFp4wH3vBTy/7lD2vRewvsx7b9CgHe89V5n33oABbN68ucz6o446imuuucZ9OmX3DR4+98aNc957UPa917cvAEPHjSv/3nPXAVwNHA073nsBdRg2bBj9+vWL/XMP97339dfcdVf571Mjfu698gotW7bkrbfe4umnny63PuLn3ujR1KhRg6eeeoq33y7/7hs7diwADz74IJ9+Wvbdl5+fv+O9d+edjBlT9pMv7vdehw47PveGDGH+/DKffM7n3qOPAnD66afz559/llnfu3dv7r3XefcNGjSIVavKvvsOOeQQ/vtf590X03sPv889p1CZ9Un/Pzfe957v/9wMfO+F4uke66qmqt97XRHhOp5QF29Ay4Q5yZPVo+CUwYO5/a67WblsCY0bNUzovkOJ5vXavHkLX333QxJrUykVsOPa+EugDxAYkIOV2TPSdv6zNlSrVi2xtQ5h3LjgP/ftS5mLTwCGDYN+/cC9OBoX0C9q//139HYNXFfGV19BkP/kIXQv2r59gSVLoGVLeOstCPhPftw4aNw4xHa+5zF6NNSoAU89Bb7/5P2fo/ufPA8+CJ9+WnZdfj64F5jceWf5c9OwIbz33o7fA9fvvPOOny+/vPQclqMKQ4ZAwAVm6TqA00+HgAtMbrgB3AtMBg0C/wvMvn3hkEPAvcBkwADwXWD66nnUUeBeYJZePPo/h5NOgosvhk2b4Igjyj+/s892HitXll8HMHSo8++SJRDkArPU3LkQ5AITcJ7/jBkQ5AKTH36A/faDCRMg8AKzb1949FHo3h2+/rrse89X12efhY4d4ZNPwHeB6f88Xnml7Hsv8Dm++y40agQjRwZ//qNHO/8++eSO916w5+d774Vaf+edMCbwa53gfze+arz2mvMIto6+faFDB3DDTZn3nq9Q9+7O+YMd7z3/59i7d9n3XuDz93vvff55+XqOG+e89UL97Z90UoTPhRdf3PHeCxJuePNNGDwYli4t/97r2xeuvhqOPhrmzSvz3vN9hpX7XAxYtv/+AesDn7/fey/Y52KnTs5LO3Bg8OcI4Z9/sM8937px4yDY7J/+zyNw34HP0X99sOfvW3/DDeXXgfPeC/X/wrhx8Nxzzvrnnitfz3HjnNMX6vn37h3+3Nxyi/MItm7cOOdx7bXl15U+0cDPPb96+f495xy/7Sj/f+rJJzsfe4HrfL+H+z/Tt37PPYM/Rwj//ENt53v+nYLMuOpbB9CqVfl1FU0ijQxckXp0211//Oy9cstTOaJ3NOE6nYNZLFIZrisyWPvLpNcwUa/P0KFDqVevLv93+38jF45RLK/RMy+9wbD/e5juu3Vm3MTJU1W1ZxKqFpSI9Dipc5spd/XtEfW2x707hjn/rqmwoand7tyPq+pMEekP7KWq/xepDM5AlGG389ezZ0+dMsXLDIvxied20GDb+rYLty4Z9bHbWk1Vloy/xWQI93ca67pkifaceqlPMp5/POct1vdNItd5qWsy3hsV8X9fOp2bdPt78yciIa9JbfjaCKrytFyp6hqcLqE6GftLJinaWPqIZx93/vdG3njnfSZPmZbA2jmytqyP+Zzu3KwJTRo1ZPTrzye4VpXeBpyewQC1CP45H6yMl+2MMcYYY0wQGXfhVBFBt6qH60QEuHD7j1WyQnAmhWufWF4fX/nGjRry0D23c8FlV7Fly5aE1CeeQO1z5KEH0bZNK/73wisJqVMVMhWnGzdAN+APj2W8bGeMMcZ4phr8YUxllFH3WMcccDcXll+WXye+ygSRCQNhxStSeMvUUcUDZeprGesXFYOOOZp3P/yEu+5/hLtuuTHm4yfySwkR4eHbb+KAgYMTts8q4kNgvIg0xxkH6mQRuUtVh4Upsy+gQZYZY4wxxhgPMipYRy1YoA5c5zFgex3QLBMGwkqmVHUfT0WrcqaG61iICI/dfw/7FBzGPnv34OgB/aPeR6JfE9m6kQ4tGvPxiMfY//izE7pvL7Lz8qhWL/rXX7Kzk1Ab71S1UEQKcGZzuF9VVwAzI5RZBxBsmTHGGGOMiSxjgnXSumNbwM4oqe6mXZXCdZOdGvPuqy9wzMln0KTxS/TquZfnbRPaUh3wt75P9z0Stu+qwp0mMcTQvqHLeNnOGGMSyboFG2Mqi4y7x9qzcK3Vocr7Hh7I1o2ewn4i7jU1DjuPyddzr+4Mf+JhTjzzPBYu+t3TNol6Xbz+TRljjDHJYPcDG2PikRHBOuUX20kM2BYOY1OR562qvW5HHnYoN197JYPPuiBi2XjPi+/vxwK1McYYY4zJZBkRrKMWbWt1uP0kOGBD1Qtq8UqXc5Uu9UiF4wcexfIVK8KWSUSoNqaqsJYwU5XZez/x7Jwak37S/h7rtLj49g/XEe7F9noPNpQNJpl+H2+ynku6hdmqcu98UVERubm5Sdl3WvxNG2OMMcakKfuiJDOlfbCOWqJaqyPtP4EBGzInsHkJupHK+D/HdAvOXlXmQc1UlQk/TiYvTLCO9XWzUG1SyS5MjDHGGJMqlS9Yp4rHVuxYAzZUbMhOZuBN1r6jCW1eX49wMuXLkGi8++HH3HX/I2RnZ/PAXbcFLWOhOn4iMgLoAoxS1btiLWOMMcYY71L9hat9wVu1pPU91lJSEt0GyW6tDnfcCMeOZYCmVN6L7T+4Wqa1IsdybhM5YFamna9w3n7/Y/ofUsCU777i2KOPKLfeQnX8ROR4IFtVewNtRaR9LGVMfOz+RGOMMcYkkrVYJ5KHbuLRtmBD8DATbytpZQmD8QY22brRWq/9XDLkXC69+gb+7/b/IiJl1sXynklIoK6gL8yyq+dSvaG3+e39ZeVkISJDgCF+i4er6nD35wJ2zBX9JdAHWBCwGy9ljDHGpEgmffmWSXU1pjJJ6xbrqCTh4rt43arYNvTYgh2PcC3MgeuCPSqDdGwFzfRze+D+vWnWdCf6HT2IHyZNLl1eIaE6ilH5042qDlfVnn6P4X6rawJ/uT+vBpoE2YWXMsYYY4wxJk2kZYu1iBwNHL1r6529bZDgi2//QB0sXGfXbehtRxHuww4MHvG0nFZUoAsXnhLREhztMePZX6LqG+q1yITWbBHhs/ff4o3XXuOcCy/h1OMHcsf1V0S3j0QE6sptA5Dv/lyL4F9weiljjDHGGGPSRFoGa1X9BPik5x5dLghbMIEX4NG0TocqGzZwR9FNHJIXSmMVS1hKZOhORet0orqFhxLuy49EhO54Br7z3zYLOPOk49iwaROzfpnreR8WqD2bitO1exLQDZgXYxljjDHGGJMm0jJYe5Kgi/CYu3tH2FfIkB3laOLBJCP8pbpbdTp244bEt157legeB4nY34w5v7LXHrtFLGeBOmofAuNFpDkwADhZRO5S1WFhyuyb8loaY4wxxhjPMi9YJ+AiPJFh2ssxPIVsiDg3tk+6htLKJNmt15lg5s+/cs7Jg0Kut0AdG1UtFJEC4FDgflVdAcyMUGZdiqtpjDHGpC0boM2ko8wK1hkSqsMd01N3cfAcsg2xvy8inOOKar1OB2vXFbJg0R907dKx3Lq4AnUVDdOBVHUNO0b9jrmMMcYYY4xJD5kTrDM0VIerg+eQ7a8iA3eSAmxMEhHQNhd6qltVDNijx4yjb+9e1KxRo3SZBWpjjDHGGGOCS/9gnUaBumT9Ws9ls2rXi1gmsF6eRhsPdz5iDbDJDj3B9h9LXZNRTw+DyvlUpe7hH372JccOOBSwQG2MMcYYY0wk6R2stTjuXcQaqqMJ0V63jxS2Pbdmh5JJISbd6mqt1yz8fTEvvvkucxcsYuwPk3jmzhtiC9Xp9trGKLtaLtXqRz9au2RnJ6E2Jl3YfX3GGGOMCaZSz40aS6guWb827lAdad9ejlG8blW5h0myKAJhZRtArqhwDSeddwmb1hdy2tH9mTrqDRrWrxfdTjYXVppQbYwxxhhjTDTSu8U6DrGG6lQKPF40Ldo+MbVsJ0C481vRdYrr+FF2DYfMbb32/3LgzieeY5dWLXjklmsRkeh2lOQwbV8qGWOMMcaYdFcpg3W0F+Jxd/sOOF5WjMHOvx5e7tGG9AwdqfwCINixIp0Tz/eyRzn9WToE7Fha0ifPnMMLb33ItNFvRBeqkxio0/F9bYwxxhhjTCiVJlinuoU6MEx7WRdN4I62NTvdxTRQm4f9xLuPiCOzRzHIWqoHN0tEd/TNW7Zw7jW38uit19K0cSOPGyU+UFuQNsYYY6oWG7PCVDYZHaxTPTBZuDAd6/Zew3aoOmdq4E6XIFW8blXCw3Uw8QbuZN3Tffujw9m9YztOOqp/+IIJDtPp8vobY4wxxhiTCBkTrBNxIV5RgdrrvmPpQu71OWVqAE8FT+Ea4pqPOx0HO5s9dwEvvfcxM0a/FbyAhWljjDHGRGAtz8Y40jpYa/H2uC/G0zFMez1mrPdqB913kgZmqyyBPWK4hoQE7HRRUlLCxf+9l9uvHEqTxu7ztiBtjDHGRM2CpTEG0jxYxyOWIFkRYTqccPVJZOiOR6TznIrgHcuc4cF4HlW8EgTsF197i5Lt2zj/mEMSGqgtTFcsERkBdAFGqepdXsuJSA6wyH0AXKaqs5NeYWOMMcaYSqLSBetoA3W6hWmvvNQ7HcJ3ogdh8/r6xnNcT63XEPX91ykXIjD/u3otwx57ns+fe4CsrPinsq+sYTorN4/cOtG/vpKdnYTaeDiuyPFAtqr2FpEXRKS9qi7wUg6oDbyhqtenut6RWEuQMcYYk5mq2v/hlSZYV5VAHQ2vzzGVATzVc4WHOq6XOcM9h2uo+IAdRavz9Q8+w2lHH0q3Tu1iPlxlDdMZrgB42/35S6APUC5YhyiXDxwlIgcBs4ELVXV74IYiMgQYAtCqVasEVt0YY4ypWFUtBJrES1iwFpEBQGdgnKpOTdR+Q8mUrt7bCuPrZhtLi1m0Ip2XdGj5TjTf+ydcwPYcriH1ATvG7tsff/M9YyfPYOZHL0S9rYXp9CIizwId/Rb1BUa4P68G9gqxaU3gr4ByY4B+qrpcRF4GjgA+DtxQVYcDwwF69uxplyDGGGOMSbhM/ZIjYrAWkbrAm0A2sBEYDCyk/L14uwOPAEOBoMFaREYCy1T1JhG5DUBVb4tUh3hbOZMdqOMNz8nYdyIDeTzThKW7kvVrExeuoXzgTWTQjvNe6AWL/+TCWx/iwyfvpnbNGiHL/bt6LY3q16WkcHVcxzPJpaoX+v8uIo/htDwD1AJC9fPfEKTcLFXd6i6bArRPbG2NMcaku0wNM6ZqSef3qZcW69OAh1X1KxF5GriB4PfifQtcCbwcYX8XiMgdnmpXXBxXqE50oE5mgE40L3WNJ3yHOreZGLi9hGvwMKhZMNEG7QSPzO2zcdNmTrz8Fm695Gz26dql3Hrfcxz9w1ROufkh+nTvzFPXX0TrZo2TUh+TFFNxunVPAroB86Io94qI3A3MAY4F7kl2ZY0xxhhjKpOIwVpVn/L7tTGwlCD34qnqFJyWjkjm4IT1pElEoM6kEB2rwOeYiFbuZAfuZN03HilcQ5wB2ydJwTkcVWXo7Q/TvXM7Lhw8MGSX7nfHTOCqR17g8yduYfz0X9j7rKtZ/Mlz5FerlpJ6VtT995XIh8B4EWkODAD2FZEuwKmqOixcOWAW8DogwMeq+nUqK26MMcaY5Evn1t7KwPM91iLSG6gPfAW8GOlevDCeBK4DRoc4TungOC13ahDFbuML1FUhSEfiOwfJuK871fe3l6xblZRwDQkK2Cn05IuvMXvuAr4bfk/I7t0jPx3D7cPfYvRj/2X3XVvTsG4dnnnvc6rn5SWkDhaak09VC0WkADgUuF9V1wHrgGEey3VNZX2NMcYYYyoTT8FaRBoATwCDgBVx3ou3ApiLMzLt2MCV/oPj9OjQxtP3KrGENgvSoW0rLEzJoGnpyGu4hvQM2P6t0cXFxTz+1igeevVDxg6/mxrVg7c8fzRuMnePeIcv/ncbHVo1B+DzidM4bN89ERHPx7bwXPFUdQ07RvyOu5wxxhhjjPHGy+BlecA7wI2qulhE3k7AvXiPANMIEqy9iLX108K0d8lsvU6VWFqtYxH1AGdJOH6g+UuWMeTuJ8nNyea75+6lbYsmQbfduHkLVz/6Ai/e8p/SUA3w7tcTuOHsQWGPa0HaGGOMMSa9WHfviuOlxfo8nOlYbhaRm3EGKXuFOO7FU9XpIjLOa/l4uxFnaqDeumZ96c/V6teukDqEOneZEriT2SXcXypbr8NNe1VSUsKT73zGvSPf5eZzT2TooMPJygo1ODTcO/I9+nTrzAF77hjQbMHS5fy+7G8O3adb2X1XsSAtedVj+mJGsrOTUBtjjDHGGJPOvAxe9jTwdMDi22M5mKqe7fdzQcTyxdurxH3T/gE6njKpDN/hzm2mhO5wYgnX4G2u52jDt9f5o3/7cwVD7n6SElW+e+4e2u3cLGz5eYv/4sVPxjDl5YfKLH919FgGH9SL7M0bKImqpsYYY4wxxlRNngcvyyTpHKi9BOR4911Rrds+8Z7/RAfzVHUJ98prUPaqaNs2Hn/zUx5+/WOuP+t4Lj3xCLI9tJre/NSrXHvGcTSpJqWt0cXFJbw6+lvev+OyhNbRGGOMMSYVUt0V2rpeG59KE6zTNUwnM0hHOmZFB+xYJaP7eaq6hKfat1Nmc/lDz7NL8yaMf+5edt25adjy/t25165bx+7Ny56TcTPn0qhuLfZo2zIZ1TXGGGOMMaZSyuhgnW5huiJCdDhb16zP2HAdTEUMqJau4XrZv6u5/omX+HHOfB684hyOPmDvoCN4h7svuuPOzZj/5woO3mvH/dWvfPUDpx+6XzKqbIwxxhhjTKWVccE6njCdbsE3FcK1Xk+Yv4SFK1ZRo1oeNarlUrdGdfZttzPZYQa7SgexBux06xIejS1bi1i+ag3LV65hwqy5PPzaR5x/zKE8e9PF5abR8jrIWPuWTZi7ZHnp7+s2buKzH2fxwEUnJ7LqxhhjjDHGVHppH6wT0SpdFQN1oGAB+5kxP7FsTSGtGtZj49YiFqxYxfG9ujDs2IIKqmV0/N8bXkN2rF3CAyWjFVtVmTH/d/bs2BaAGfMWccXDI5i3+C82bN5Cs4b1adaoPm1bNOXbZ+6iY+sWlKxfS8m2zTEdr3/P3el/zQPccOpRNG1Ql2c//pbD9t6DRnUrTy8HY4wxxhhjUiGtg7UWF8e1vQXq8vy7h3dt2YQW9etw78mHAvDPug0ccMfzFHTehT4dW8e8/2gkqqv6tsLClHcRDybWwL21aBtD7nmSd8dM5Lozj6N+nVrc99L73HfZmRzeey8a1KlFVlZWmePGO/1Vp1bNOfOw/bnu2bd47NLTeOKDr/jm4Rvi2qcxxhhjKp+KGKDLBgUzmSatg3WsLFCX9dfa9TStU5PsrCykdg22F5eQJcKn0+dxaf99S8vtVLcWT5x1FBeN+Jgpdw+leq73t0es5zxwu3iCttdwncwu4bGE3dWFGzjlzqepV7sGc168h5PveJKc7Gy+e/RGdu3Q3tnnxsKkTH1102lH0+PCWzn1rqc5qnd32kcY/KxKyc2L7YuS7Er5sWqMMcYYY8KoVFeAFqjLm/3XPxz++NvUrJ5H385tWLamkBVrN3BgpzbkZGcxqFeXMuX7d21H+6YN+WTaXE7cZ3dPx0jkeY83aKe65TpWRdu289XUObwxZhJfTfmZcwYcwN3nnUB2dhbfPXYT2VlZ5Vqok6FG9Wo8dunpnHT7/3j26nNKl8czf7wxxhhjjDFVTaUI1lU9UKsqfxduZO6KVcz9ezXz/17NpqJt5GZnMf/v1Vx7dB/O7bsXo6bPY8W6DRyy2648O+YnHjj1sKAjSZ914J68MHaap2Cd7HOfrKnDYmm1Xr9pC59OmsEXk2fTpmkj9t+9Pft2aUftGtU9ba+q/PjrIt78ZhLvffcT7XduyikH78tjl51Owzq1Ssvl5qT2z7Jfh2YsGj6MetUsUBtjjDHGmLKsW743aR2si0siv4q+4LVm0xbqeww4lclZIz9l4qK/yMnKomPTBnRq0pA9WjSmdvU8qJ7HgQrH9uxMrep5nN13r9Lt9t61Rch97t+hFVe+8lnEY6fyC41opg5LdKt1SUkJz48axx0vf0SvTm05qnd3lv67mgfeGs20+YvpsHMT9tu9A0f17kZB986AE6I//H4asxct5bdl/7Bw2T/89tffNG1Ql1MO3pfxj99Mm6aNE1bHqJ5PkPBcr2Z+BdTEGGOMMcaYyiGtg/WCFatYsXY9TeuVD1Rb16xn6/btfDJrIS9NnM2UxSuYdvM5NKtbK8ieYMsqb6OLV2+Y/t2I/U1ZvJyPLj6Bzk13tL7G27rbqHYNNm0tYuPWImpWywtapiJ6CSQ6XHtptZ67ZBkXP/oyqspXD15H59bNy9apaBtTF/zBfa+P4p7XlpQG6/Gz5nHds29xxqH7cdjee3Bxi51o16JJmZbpVLPWaGOMMcYYY5IjrYN1vZrVGXDfK7x3xcm0bdKgdPn83/7k5UlzeGPyL+zWvBEXHbgnL02czfQlf9Nsj7LBxWug9lI+HUN3g4BW+kR0mRYRDujUmjcmzOL8g3qWW1+RXe+jCddehArXRdu289Dbn/HUR2MYdsYxXHBkX7KCzO9dLS+XHu3bMGPhYj646/LS5e+Pn8oFR/blulOOTFhdY2WB2hhjjDHGmORK62DdtG4tTtu/GwfeMYK6NarRuUVjthdtZ9af/3Bij058cskJ7Nq4PgA/L1vJjD//5og9dgWiD9ReRNpnRQTv2tWrsWbj5oTfg/zf4w/ixEffZHDvPahdvVrp8nhCdbDzF8s58xquY+0SPnX+Hwx58AXaNG3EhP/dQsudGoQtn5OdTXZ2Nvl5Tut+cXEJH34/la8fuj7qYyeKhWljjDHGGGNSJ62DNcAFB/fkvIIeLFm1llnzl7C5aDsvnnUkNfJyy5Tr3rIJz38/IymB2qtQx05W4H5n6q+sKNzAHh1bJXzf3Vo15fBu7bls5CheGHIcWVkSVaj2+jr4ykV7jhLZcu3faj3ztyUcN+wxHhh6MicV9Ao6uFug7OwsBh+0D29+M4nbzzmeib8sZKd6dWjXoklC6hcNC9SZR0RygEXuA+AyVZ0douwIoAswSlXvSlEVjTHGGGNMBGkfrAG2rdtAs5wcmnVpG7JM2+rVmLciPUNFolpq/X0z9w9u+/R7Pr3uDBrVrhnXvkK5/9TDOO7h17ntvW+4uV+vsGXj/UIjloDtJVxHM7f10i1w/C1P8Nhlp3PcAT081wPglIP3ZeDNj/DXyjVMnf8Hgw4s34U+WSxMZ7yuwBuqGraLg4gcD2Sram8ReUFE2qvqgtRU0RhjjDHGhFP+ptE0E6mVdMuqQrasKiQnSyjJoLHgYw2iy9au59ZPxnPJG1/y2qUn0al58kaWrp6bw2uXnMCoaXMZOWFWyHKJ7CXgez298tKKvq0w8v7WbNjEscMe5YpB/aMO1apKrfxqHLJXF+YvXUGNannM/G0pt774PprE92TJulUWqpMoK68a2XUbRv2Q7GxEZIiITPF7DAlzqH2Bo0RksoiMcFuwgykA3nZ//hLok8Cna4wxxhhj4pCWLdYicjRw9C7u/dOetyNyt91MNf/v1Tw5bhqfz/mNk/fuwvjbLqBFg+Tf092gVg1eP3cgRz/1LvP+Wc0+bZrTu20LmtRJTiu5z5ZVhZ5br+PtFr5123ZOfuBl+u2xK5cdf6jn7Rb8uYL73xzN2Blz2V5cTEH3Tpx7xIE0a1iPTVuKuO+NT9ltl505qSB8a380LEhnBlUdDgwPtk5EngU6+i36FuinqstF5GXgCODjIJvWBP5yf14N7BWkjDHGGGOMqQBpGaxV9RPgkz3bNLvA+zbg4XZYtq7d0cJZLcg0XqniNTROXbKCJ76Zwk+Ll3Peft2YdMNZNG2R2vmP2zSqx0dDT+DT2QsZOXEWD371I99fe0bSj5vIcB2uS/gb300jNyebe8840tMUXOAMcHbCrU8wdODBXDv4CNrv3KTc/djzly7nmY+/iTtYW5iuXFT1Qv/fRaSaqm51f50CtA+x6QbAN+F4LTKgx5ExxhhjTFWRlsE6Fl463PqHav/fKzJgh/PYNz8xcsJsLj2oB0+dehg18nITPvq3V20b1+M/B/dEUZav3ZCy40YTriMJFa4/mjyHsw7eu3Q6rUjhevqCxRw77FGeuuIsjt5vz6Bl3h47mWc++ZZP770q5H4sMBvXKyJyNzAHOBa4J0S5qTjdvycB3YB5KamdMcYYY0yCZdAdvJ5ldIuH/724WQLbi0tClg0M1YHrwq1PNC9B8bnvZ/D65F/4/D+DOW//bhUaqv21b9yA7xYuZVPRtpQd0+s917Hcb124aQsT5/7BYXt2KrM8XOj98dffOGb/vUKG6sV/r+LK/73Gp/dexW5tWpRbb/dGmwB3AK8AM4CJqvq1iHQRkcBRvz8EzhCRh4GTgFEpraUxxhhjjAkprVusNUxQDtSkVg2KVVmxfiNNA0bJ9hqaU9GC7SVUvzb5Z54ZN50PLx5Uei9zOoRqgCP22JWPZy3g3s8ncufAA1N2XK8t19GOFL5242ZysrJYt2kzdWpUL1MuVMt1frU8tm0vDrn/kZ+P5+RD9g0Zqiual8HcohFu1PVEH6syUtU5OCOD+y/7BRgWsKxQRAqAQ4H7VXVd4L68TsclIk2Ad1X1gGi3NcYYY4wx5WV0i7U/EaFni52Y8uc/ce8r1S3Y/j6YMZ/7v5jE20OOpWV9J7CkS6j2ufe4Aj6euYAffvszpcdNRst1q8b1ueKYvpz3xFsUl5T/IidYEK6Wm8vmEC32C/5cwcjPx3PegLJfOqRDK/W2wsKkBF3ffoM9TGKp6hpVfVtVVwSu85+OC2grIkHv1RaR+sBLOIOhRbWtMcYYY4wJrtIEa4CeLZvwU0CwjicgJzpgR2px/fznRfz3o+944/xj2NUdET3dQjVA/RrVefCEg7n8ra/ZsKUopcdORri+amBfcnOyuf/9b4KWCwzEDevUZMKcBbww+jsKN24GYOW69Vz55OscdOX/cdWJh9PFr7W6IgO1l5C7YMUq9r/tOfYe9gy9hj3DJS9+wrxlKykuKeG+T8bzw/wlKayxiUMB3qbjKgYGA/5vCk/b+k8j9u+//8ZbX2OMMcZkONXgj6oorbuChxMsYO29807898tJpb9XVKtzMJFC9fvT5zHso+947byBdGnWCEjPUO1zaOddGD3nN+4a/QO39Y1u3udUiaZb+POXDGa/6x+j7+67sl+nXcqV84XjrLoN6ddjNx6/7HRe/vIHbnr+Hdo2a8zSf1dzQt9ezHj+ThrVrV1uu1SKtqX4pe+ms1+HVpxf0IPtJSWMmj6Pox54lTo1qrF8zXoW/bOG/Tu0SlJtTayCTNvVFxjh/hxyOi5VLXS391/saSov/2nEevbsWUX/2zTGGGOMKS9jg3UwuzdpyO+rC9mwdRu1quUmbL9b166P677rcKG6pER58KsfeXvqr7x74XGloToTXNd/Xw5++HVuPXCvclNNhRPvveyJnuN6W2EhzRrU4amLTuDcx99k4v2XU79WjaBlfUH58M47c8S+F7Ny3Xp+mvs7uzbfiQ4tm5Yrl0qxdL0uLinh3R9/5sOrT6Vjc+e9t9vOO3Fp/335ft5iJv/2J2s3bUl0VTNDTh7kxzAifVZ24usSRJBpux4j9um4bCovY4wxxpg4VKqLp2o52ezWpAEzlv+b8NbqZLR+byraxoWvf864BUsYfdngMqE6nVurfZrVrUWNvFx+XxPbvbTxnFOvXcK92lZYyIAenTmqZxcuf+4D1EMflpJ1q2hAEYd1alGhoTqe+5nHz11Mk7q16NS87NzoNarl0r9rO0bPWMAxPTonopom+XzTcYEzHdcfKdrWGGOMMabKq1TBGqB788ZMWfRX5IIxiCUIhmpZnfDbnxz08Ovk5+Tw3oXHs1PtHS2kmRCqffZq3ZRpf3m/1zLUXOKxSOT91uAE1LtOP4Jf//yHV8ZOiaouvsHJUhWqEzVA2KfT5zGoV5eQ6w/s3JovZy+M6xgmZT4kYDquENN2edo2abU0xhhjTNqx+6TjV+mC9Z7NGzPrn9UVXQ0geKjesKWIG97/lqGvf8FtR/Xh8ZMPpXrujh75mRSqAXq2bsr0Zek/iNHWNes9BezsLZsZefkp/PfVz1i4fGUKahadRI+2vWLtBlq7A+UFc2n/fXn1+5ms2bA5Ycc0yeHeO10ATAIOUtV1qvqLqg4LUb4g3LZJr7AxxhhjTCVS6YJ192aNmPn3ak9deZMpWKiesng5BQ+/xubtxYy7+jQG7L5rmfWZFqoBNm7ZRrUc7/eUBr4u8c4ZHm2XcC/hukO9GtxwwiGc+/gbYeerTqVkTV+1dtMW6gXM3+1v5wZ1OKxrO175fkbCj20SL9x0XMnc1hhjjDGmqqt0wbp5HWdq1uVJaGHzGgJDdf9+aeJsztx3Dx47qV+5MJOJoRrg2/mL6btLi8gFgfdmL6T3S6P4PcH3qycjXJ+33+40qlOLu97+KtZqJUSy54Neu2kz9WqGDtZOmS00jfMLEGOMMcYYYyqzShesRYQ9W+zEzAroDl69YZ2QoVpVKdxSRFM3+PvL1FBduHkrPy9fyYHd24ctt2Xbdq7/7Af+N3EW/Tu24vEpv6SohqFFCtciwv/OOJw3x0/jltc/Y+u27SmqmSPZgdpn7cbwLdYbthTxw7zF9O/aLul1McYYY4xJRzZXs/Gi0gVrcAYwS3WwDhWotxUX88msBfR79A3+XFNIr12al1mfqaEaYPzCpezduhn5uaFnbZu+7F+OeXkUG4u28clZR3PLIb2YvGwlv65cm9C6xDJKeKRw3bhOTb69+Rzm//UvB9z4BDN+T86geD6JGpDMi3d+nMPCFatYF6Er+NdzfmPvXXcOW8YYY4wxxpiqrlLNY+3TvXkjHp6/uELrsHj1Ol778WfenPIrrRvU4ZpD9+Hw3dqWme85k0M1wLfzlnBQx9ZB163fWsT946bx2bzFDDt4b47pskvpc79wz448+tPPjDzl8ITWJ5r5rX184TrUa9G4Tk1eHnIMb02awzF3j+CSI/pw7XEHRTVvdzipCNHBPDvmJ+YvX8Wmom3Url6t3Ppt24t5Ydw0Hvz0Bx447bAKqKExxhhjjDGZo1IG665NGzFv9TqWb9hEs1o1Im/gQbj7q31h7sMZ8/l09kKWri5k6ZpCTtirE+8MOZaOTRqW31+Gh2qAYi3hybFT+adwI8fv2ZHWubnkZAmfz1/CbV//SEHbFnx9/rHUyy8b3E7s1Ib7J81me0kJOVmJ7TQRS7gGJ2CHek1EhJN778EBHVsz+Im3qJ1fjaED9o+rnhUVqH3y83K5+NBeLFyxmqyssl8SFG7eyqH3jKR5/dp8ePWp7LbzThVUy4ql2TlotfK3bkTcTiplRyBjjDHGGBNGwoK1iAwAOgPjVHVqovYbSvWGdUJ2/61TPY/zu3Vg2LhpPH/E/glrXQxn87bt3PjBWG47+gDaNqrHHi0al5lGy6cyBGqfR07sx/y/V/PWlF+54NXP+GtNIY1q5pOfm8MTA/vSq2WToNvl5+bQKL86ywo30ioJg2IlI1wDtGhQh1eGnsCh945kz2YN2Ld756j2X9Fh2l+NvFy6t27GDQMPLLdu6qK/aFArn/evPCUlfzvGGGOMMcZkOk/BWkSaAO+q6gEikgMsch8Al6nqbGB34BFgKBA0WIvISGCZqt4kIrcBqOpt8TyBUC4t6MnAFz/m/XmLGdSpTTIOAexorR41ayHddt6JwT1Dh63KFKp9OjRpwH+P3J//Hrk/6/5ew5K162lVr3bYKbiq1atNm4Z1+WNNYVKCNey45zqWruHhXqdddqrPw6cP4JzhH/DdfxtQr2Y+uXVCHyOdwrS//LxcNm3dFnTdzCUr2LNNMwvVxhhjjDHGeBSxz6KI1AdeAnx9IrsCb6hqgfuY7S7/FrgSeDvCLi8QkaSPhJSbncW9BT14YNIcVsQ59ZaXabZenTyH0/fZPfj29WtXylAdqG6T+rRvVM/TvNa71K/N76uTHzqTMajZwB6dGNCtA7e99y0QfATvVA1CFq2ta9azdc16dqlbiynzFgd9rjMXr6B762YVUDtjjDHGGGMyk5ebAYuBwYAvJewLHCUik0VkhNuCjapOUdUHVPXvCPubA5wWc42j0LlRPU7drS23fDcNTcKY+P6toXOWrWTfts3LlakKgToWrevX4Y81qQmeW1YVJnyu6wsO6sHXc34r875K5ajeXvmCtO/hc0z3Dnw0cz5btm0vt27Nxi3Urp5XEdU1xhhjjDEmI4nXwCkiY1W1QET2Bv5U1eUi8jJOF/GPPe5jJPApcB0wGsp3BReRIcAQ99dGwEpPFYxOsvZrMkemvQfqAusquhIeJaOuK1U1scPIhyEiPc477cQpz9x/Z9Tb9jr8eKbN+rnS96MXkX+Bip1+wbtM+3tPJTs3odm5Cc3OTWh2bkKzcxOanZvQ0u3ctFbVxsFWxDJ42SxV3er+PAVoH+X2K4C5QAEwNnClqg4HhsdQL89EZIqq9kzmMUx6y7T3gIgMV9UhkUtWvEyqq4ldqP9U0lGm/b2nkp2b0OzchGbnJjQ7N6HZuQnNzk1omXRuYpkX5hUR6SYi2cCxwMwY9vEI0DeG7Yypqj6p6ApEIZPqaowxxhhjTNxiabG+A3gdEOBjVf062h2o6nQRGRfDsY2pklQ1Y8JqJtXVGGOMMcaYRPAcrFW1wP13Ds7I4FFT1bMD91dBktrV3GQEew+YsDQ7j5LqMQw+mBV5VHyTcvb3Hpqdm9Ds3IRm5yY0Ozeh2bkJzc5NaBlzbmLpCp7x3Pu4TRVm7wFjqg77ew/Nzk1odm5Cs3MTmp2b0OzchGbnJrRMOjdVMlgbY4wxxhhjjDGJEss91hlLRAYAnYFxqjq1outjKpa9H4wxxhhjjDGJUClarEVkqIiMdR8zRGSEiCzxW7aHW3R34HFgvzD7uk1EfvXbtnsqnoOJnYg0EZHxfr+PEJGJIjLM/T3H3g/GVF7+nwEisouIjBKR8SLykLss6GdA4GdFZRTp3PiVe0pEjvb7vcqfmyDXFs+6y+3ciNQXkdEiMsV3Xtzldm5C/J1V9nMjInVF5DMR+VJEPhCRvGDP2euyyiKK81LmWtZdVmnPC3g7N8HKuMvT8txUimCtqk+raoE7INp44FngDd8yVZ3tFv0WuBJ4O8Iu7/bbdkbSKm7iJiL1gZeAmu7vxwPZqtobaCsi7XEG27P3gzGVUOBnAHAfcKeqHgDsLCIFBPkMCPFZUal4PDeIyAFAU9+I/nZunHMT5NriOTs3pe+bM4DX3Llla4tITzs3peem3LKqcG6A04CHVbU/sAI4mYDnHOw8VIFz4+W8BL6vqsTnMB7OTZAyh6fzuakUwdpHRFoATYCewFEiMtn9RiMHQFWnqOoDqvp3hVbUJFIxMBgodH8vYEdQ/hLoA+yLvR+MqawCPwM6ANPcn/8B6hL8M6CA8p8VlU3EcyMiucBzwB8icoy7rgA7N3V9BX3XFqo6BTs3vnOzCthdROoBLYGl2LnxnZtgywqo5OdGVZ9S1a/cXxsDp1P+ORd4XFZpeDwvge8rqOTnBbydmyBl/iGNz02lCtbAJcDTwE9AP1XtBeQCR0S5n5tlR/cvmzsnjalqoaqu81tUE/jL/Xk1zhct9n4wppIK8hnwLnCrON2aDwfGEPwzINhnRaXi8dycCfwC3A/0EpHLsHPjOzc+vmsLsHPjOzffA62B/wC/4pwLOzfOuQm2rNKfGx8R6Q3Ux/myJfA5BzsPVeLchDsvQd5XUEXOC0R8z5Qpo6qTSONzU2mCtYhkAQcBY4FZqrrcXTUFiLaLgH/X3+IEVtMk3wYg3/25Fs573N4PxlQRqnoX8BlwPvCSqm4g+GdAsM+KSi3EudkTGK6qK4BXcf4ftXPjnJvAawuwc+M7N7cCF6nqHcBc4Bzs3LykqhtCnK8qcW5EpAHwBHAuwZ+z12WViofzEkylPy/g7dwElCFUuXSQNhVJgAOAH1VVgVdEpJvbungsMLNCa2ZSaSo7uoR0A/7A3g8mFlk5aF7NqB9IZfpYzVgzgFbAw+7vwT4Dgn1WVAUzKHtuFgJt3Z97Aouxc/Ow3zL/awuwc+M7N/WBPdy/qX0Axc7Nw2GWVfpz4w4q9Q5wo6qG+hzxuqzS8HhegqnU5wW8nZsgZQhWLmWVjqAyTbd1GPCd+/MdwOuAAB+r6tdR7utmETnf/flpVX0rQXU0yfchMF5EmgMDcO6tnIW9H4ypSq7FGexkk/t7uf8TRKQO5T8rqoLAczMCeEFETsbpJn8CsB47Nz7+1xYQ/P+YqiDw3NwLvIjTHXwi8AZOY42dm+DLPqTyn5vzgL1wrpluxnl/nBHwnJXy5yHYssrEy3kJ5kMq93kBb+cmsMzTpPG5kR1fwhpTObijKx4KfOd2bzQmKiLS49yzz5ry1BOPRb1t7wP6Mm36DElCtUyC2WdFaHZuQrNzE5qdm9Cq4rkJ9py9LqvMvD7fqnZeIPPPjQVrY4wJYMHaGGOMMcZEw24GNMYYY4wxxhhj4mDB2hhjjDHGGGOMiYMFa2OMqWAi0kRExgcsGyEiE0VkWIRtPZUzxhhjjDHJY8HaGGMqkDsAx0tATb9lxwPZqtobaCsiQede91rOGGOMMcYklwVrY4wJokSyKMrKi/qhCCIyRESm+D2GhDlUMTAYKPRbVgC87f78JTvmawzktZwxxhhjjEmiyjSPtTHGpAVVHQ4MD7ZORJ4FOvot+kZV7xApM5B4TeAv9+fVOHM4BuO1nDHGGGOMSSIL1sYYk0KqeqGHYhuAfPfnWoTuXeS1nDHGGGOMSSK7CDPGmPQzlR3dursBf8RZzhhjjDHGJJG1WBtjTPr5EBgvIs2BAcC+ItIFOFVVh4Url+qKGmOMMcYYa7E2xpi0oKoFfj8X4gxMNgk4SFXXqeovAaE6aLmUVdgYY4wxxpSyYO1HAkYP8roumUSkVhRla/rqKY685NUsdSrq3KdCZX5uJj6qukZV31bVFYkolwr2GWqMMcaYqsqCtUtEBgOXhSlymVsmln1XE5EjRaRblNt1AH4WkaCvk4g8LCJH+i16FRjk/twVmOV/YSgiR/nPcysiZ4nIlWGOXyea+iaDiPQB7q3oeiTRQBG5rqIrYUy87DM06P4T+hkqIt+IyKEBy/4jIge4P1cXkewo9/mOiPRIZD2NMcaYqsiCNSAirYALgWfCFHsWGCoiraPY78UiMgGnm+YFwF4isq+I/CoiM0Rkpoh86Fd+L3fO2+9F5HvgBaAeMNFd9qOIjPQ7xIvA4yKyv/v7g0B39+ejgf+papFf+RLgMxG5xv39ApxgN9Lv8bqItHXX3ygib7itOG+KyD8i8qffY6uINPV6PiKcq7EiUhCwrDZwn/vwLeskIrMDyl0qIr+JyB8icnGQfXcUkbl+vx8pIgtF5F8ReTzShaiIHCYiS0SkSEReFZFcd/n+IjJfRH4XkcMjlQ9R/49x7p/dJ/wZSh8ikiciz7rvgdkickCwZVHs73YRuc3v98NE5DMR+UREernLrhCRRSKyq/t7TAHNJEe4z1C/v237DI3wGSoiP7nPa4aIfBTklGxzH/7+AN4QkYbAaGCqlJ1DfYvf/luJyDD/B9AFuNX9/b8icod7HpuJyAT3vG136/a9u+wwEaknjs9F5Jjgr6AxxhhTddjgZY47gGsCLqDKUNWtInItcDtwtsf9dgKGq+pI3wI3cPyoqmeLSBvgDb/y1YElqnq8GyrrqepSd7sewAxVLfar02wR6aGqa0XkO6AVsEVEzsCZ33a9iNwAXKmq76jqaBGZDOwtIv2Bf4HA1pZqwDJ3/zeKyL04ow4XAQNVdZLfcxnr8TzE6hrgQVVd4x6vHfABO6YXQkT2AIYCe+N8UTRBRGao6gR3veBc0Ff3+/1BnEGeNgKfA8cB7wargIjUcLc/C5gBjAFOEpF3gLeAM4GZwDfua0Cw8sBrweqvqioilwPPAaXhPM0NwXl/tcEJIZ8A9wQuE5EOqro+3I5EpBNwA26vBDeQPAacBrQG3heRlsAxwLXAUSLyDfBPgp9TOdtLlM3bSqLerkSTUJn0Z5+hO8TzGbqHqlZ3n9coERmA8zdVjBPqmwFHikg9Vf3Q/ez4AKeVfY2qHhx4AkXE/777lsBgnC8EfL72Lw7kActVdTmwn4jUB5YDfVR1q7vPOsA697luAQoDj2uMMcZUNVU+WItINaC1qk6LVFZVfxKRtiJSzXeBEUGwq/LA1oZiv58FUHG6Hu4PnIoT3MC5eOokIttUdZuIVAf2UdVxbt0O9HtOM4C+qvqH37LaQH9VfU9EpgBjgbWUD5Q3qeo8EckBeqrqje72pwAvi8gmv7LtIjz/eB2CcxHuczEwDHjIb9lJwOuqutqt5xfAfsAEd/15OBeFbdzfGwGFqrrSLT8ZaBGmDrWA81T1W7f898DOwD7AUlX9xl0+BufieUaI8qHqj6ouFZFNItJCVf8Kf0rSwmDgBlXdDkwRkZXA/UC/gGUdgSmhduL3pccHfoubAJep6lSclrfhOK+BAGtwWtcOUtXHk/C8TAzsMzShn6HFAT+L+yh9bgHlTwTG44T2yVL2Nvb7VfXtgG224YTgzTit9Zv91tfC+Xwc5IZqn8NwvoS8z93/tzg9bYpVtchdVjW/TjLGGGP8WFdwaAv86r9ARE4Tp9vpcreFxd/PwC5R7P8mEflBRKaLyKURygqwHacl9H6gvzjdKMcCO+G0rj7rlt0d+EBEnhOR/GA7C1APuF5E3gCygYtwLsb2Br5X1Z7Am+xoTc0HzhURX9fOHOA1nNZe32NpmcqLXCgiS8Xp7niXu+xIEXnXr8wI9wITEbnFPcefAXUC9pWPE4D9L6yvxpm3N1Ajv5/3AHyheSfgOuA/futXAs3E6U66M05L6JhgJwxAVf9R1THu/gQ4GKdb6s7ALL+ii4F2YcqHqz/Aj27dM0UjKA0brXHOa7Bl4ZyPc94+9y1Q1Ymq+pWI5IrIZcBUt9W7BGjs/mutY+kl2GdoyL9t7DM05GcoUOKG+tFAiaqOBr5S1eGq+pxb/iNV/dAtvx3nbyIX+FNVe7r1+BSoG+Q5KLBdVWeq6l7uc/jBPSfFwHWq+mPANpfifME5EqgBtFRVC9LGGGNMgCrfYg3Ux2kJA0BEOgP/B/TG+TZ/toh8rKrz3CJr3G28uiegG2O4eWarA1tUdbDbzfAKnC62AO/gtAgWAajqFBHZDTgDp+vi1Tits5uADsDHIrIdyFfVzm6raB/gTpwLsUlAltsVebU48+DmAttEJNsNM0NExBdaHwQC76e+Eljt9/sDOK3FC4FX3ID1FfCUiOS4rZkFwBXi3Dt7Lk4LZDt2hE+fMq+L+5xVyg8s/C7wnYhscPezDzsGH3oMuENV//Vt5+5jOE730X+BacAvInI95bt0zlW/KZBwWr7Wq+o4ETmdsgFvI86FO8HKh6m/T7Tvq4r0GvA/cbpxHwfMxbmXtcwy/9a+QCLSBOeLhv2AgUGK3IdzQX+8+/tbwG3uv81FZBpQoM50U6ZiBX6GRvrbts/QHQI/Q0tUtbs4XcE/FGf8h3dF5GpV/ZrQSoC+bks6QHPgliDlauN03faZh9O9/CycLwY3+BcWkfOBhsBoVV3o/t1OD1MPY4wxpsqyYA2rcC4cfPoBn6rqn+7vzQPKN3K3iZqEGJnWT03gH3EGu1oCfIfTvRickFimlUBVl4vIA27rwUPuw9eN8djAYON223sepzvgVGAXvwuxgThdotcDNwLvuctfdVuJRoao8yjgZvfn74G7gQ+Bob77a8W5d3E/ce71m6Oq60VkP2CUe//0TxIwIBnlX5egVHWmiBwEnIATqp9X1VXiDCZWR1Vf9S8vzoi+xwMd3fNxK3CVqpYZJC2QiLTAuQ/Ydx/0GpwWLJ98/LqtBikfSSOcQYjSnqo+IyJ/43S13Qs4xe0eW2ZZhN08BtyiqquDfdmgqleJyIvAGBHZVVWfBZ51W7GPw/lipC/O/d2mYgX+rUb627bP0LL8P0PLPD9VLRbnHu+LKXsvdDDjVPVYt/63hShTD/hbRJ4FuuG0eFfDua/6c/dvsSPOF8vLccY/OBP4wn3evYCfItTDGGOMqZKsKzgsAjqHWiki/UWko9+i3YDfozmAiLRzu0NfELCqLmXvqWuBM+jNVcD7OFPXHO4+rscZzMt/v1nADyLSUJxpVsq9niKSLX6jUuOM2tsZ58Lpe+Ag4Be3++BbwAmq+p677V44rfY5OPcSHotzD/EVOK01l1L2i4eBwOM4F2ZzRKSxu/xdYID78HULD7xfsMy9lO79lzXEuU8xLPfezkdwzqdvaq5jgX1EZIWIrABauv/2BBbrjkGWpuB05QzJPX9vAA+oqq/791ScIO/TE/gzTPlIeuOc24ygqh8A43C69b4falkYR+KMxrwCJ/BcIyKPiUh3cUf+VtXZOH8PrQDcL1DG4rx3VlH2iw1TcQI/Q8P+bWOfoTMI/Rma7dcVPBtAVT9T1eMJL9j/5cG6x3QCflfVC1V1X1XtA5wDTHZ/vgpYpKoLVXUj0FmdgSB7u+Xed79cqOHWtULmJjfGGGPSUZUP1qq6DfjV7eIHzv22R4pIc3FGPv0fTisIItIXmO1u40VznAuoL939vuhb4bbYjgc+8yvfDVjotp72BP7CGYH5dpyLsfsD9n80zujI1XGC3hS39cTXjXEKTnC8wj3m7jituBNwWo3Wuq3Ki0Skgfs8/Z/bCTj36vlfuAYqdvddA5iD07X6FsDXNRuc+xr74AxG9rG7bDIwQETquhefXYPsezRwcphj+7sSp7XaNxrvRaraSFWbqmpTnIHGmuJc0B8oIgUi0h2nO/KcCPt+HFilqo/4FqjqCuBfcaaoGYQTFL8KVT4ctxVdVTXpI10n2I04AzVphGXlqGptv9fmcpzR3y/HeR8874ac3XC6zi5wN+vqhm3FCVTWDTwNBPkMDfm3bZ+hQfmvy1bV7sARRNejrBhoJ+4UWzi9cjYFKdebgPvh/anqZJweB77ffc+lE0438jvd30twZmOwYG2MMca4rCu441ac+9iOUNVfxJnb8wecFoNHVHWaG7LvYcc9n15Mxgmaj6rqFihtzQSYCNT1BRD3Xrp+wE0AqrpJRIbgXExuBfZTv2li3JbcO4FL1BlJeje/dTMI0o0R5x7Vx9yfqwHt/boxHoVzMdlbRLrhXDCdgTMtVY1IT9St71PAbJz7DEe7z983zc4SnAvSde6yH0TkTWA+zn1+vwTZ7aPA1yLyrYYZLdttGT+D4OE8sJ6TRORJnJalGjjdKx8Os+96OAP8rHFbVwFeU9WrgdOBJ3Du6T5HVf+OUD7Y/nPcfQQOkpfWROQIoEhVP4uwrCVOt+CIr43rFZweBItx7oE/2W0hq+8uA+eLmiE43c5NevD/DA36t22foZ74bm1YjPMlga8+tXBmNmiB033bJxfnPu+pOIOxlSEidX3l3dtTDsZpefatz8G5391/CjL/c1QNp9X/OuBE9wtF3NfiGXFGVq+FjQpujDHGIBEalqoMETkE2FVVh4dYfyFOS0jIEaQ9Hudg4ExVPTtg+X4497P9F2c6o044LSKjcVptDsK5r3cFzn3M64AbVfXCIMf4FeeicJ7fsiY4g/ccrM4gYoHbDMFpve2kquq2zDyNc0H1PM49lEtwWhBX43TDXYMzYNcIVQ02UE7c3Hocp6p3RiycgUTkSKCRqr5U0XUxO4hIj9PPOnvKQ4/9L+ptD+3bh5kzple5ljz7DE3eZ6gbgL/EGSjwKr8vGWYBQ9Rvbmy/bXrh3Drxlqqe434BeYiqvulX5i6cL6luUdVnguzjZZwvLE9V1XJfforIApxR33vojnFJjDHGmCopI4K1iAzAuadtnPvNfKXktsRUx7kAnBrYSivO9FC7A2Oi6Erp9dgHAP8EXEjmBLuANKayE5Eep5xx9pR7H3ki6m2POvgAZs+sesE6HdhnaJn6ZOP0EloTsXDofdTG6YUSdM5xEWkIrNGy0yIaY4wxVVKFdgV3u6m9idPleiNOK8PTONO0jFLVu9yiu+MMTjWUEPMAi8hIYJmq3iTuiKiqelsSq59w7oXeNnbchxy4/k/cAbKScOzxQZZZqDbGZAz7DC1z7GICpiyMYR/rI6yPaXR3Y4wxpjKq6MHLTgMeVtX+ON3zTsYZvKU30NYd1AngW5zBqd6OsL8L3Hu+jDHGRElEGojIobJj7mVjjDHGGONBhQZrVX1KVX0jKTfGGQzKF56/xJ1/VFWnqOoDqvp3hF3OwQnrxhhj/IjICBGZ6A7OGGx9fZwRrHsB38qO6fKMMcYYY0wEaTEquIj0xhmZ9A+c6VHAGdxlryh39STOQDGjQxxnCM5ALXTp0qVHfn5+LNVNiZ9nz6J1k4bUrF7NWZCdXa6MZOcELijzq2YF+d5Eyu8n2SQnl6VLl5KXl0eTJk1SfvxoqSpr1qxhxYrlqEKTJk2oV78+WQmeslUVFi36jc2bN1OzRg2aNmtKjfx8JCv1r5FXW7ZsYd68ebRr146aNWuWW7/w342lP2/bsoHNK5cjWdnk5tcgN78m2bnV0JJi1v/9Jw1atCGnWrUy2+cEe88COVllz322+/uiX2Z9oaqHx/u8KjsROR63N5CIvCAi7VV1QUCxrjgDY01yQ/ZewBeh9tmoUSNt06ZN8iptjDHGGJNmpk6dulJVgzY+VHiwduf+9E1ZdBXgS7u1iL5FfQXOqKkFOKOhluGOVjscoGfPnjplypTAImlhy5cj6Hrezbw2bCi7tWlRujyrdr0y5bLrNiy/cX6dMr9qtfLhx6ekeu246ukva0voW/Fymnfk8ssvZ5ddduGKK65I2DGTYdKkSZx66qk0a9qUh+//Pw4/rD8SEKiLsvLiPk5eSRHghPjf/1jMUccex92338aRRwygWq26ce8/Wa677jqOPfZY7rrrrqDrjx4+EYD1c8bx40sPcNiwZ2nScc/S9TvVrsbv035g9CM3ctQ197HTLp1oUCv4+WxUs/zyOtVzy/w+uFsL67LsTQHlewOVCdaqOg5ARA7EabW+I3An/l9OtmrVinT9DDXGGGOMSQYRWRxqXUUPXpaHM33Jjaq6WESm4lzwTcKZw3NeuO1DeARn3tOxiapnKm16536y6jZkj11aMmvR0jLBumT92jLhunjdquDh2o9sdVoQgwVs/zAcLGSHC8vREhGKi4sjF6xA06dPZ+DAgTz52CMMPPqokOV8oThQqMAdqjw456XtLm2oX68ea9eti67CKaaqfPDBB7z11lshyzSs5bRAT/h4JH2G3k6TjnuyU+0drdLr/lnGqIeu56y7n6H17uWntvYSpn3qVqvw7wUzSU089AYS51ukwTiDXpUbNTvwy8mk1NQYY4wxJgNV9OBl5+Fc4N0sImMBAc4QkYeBk4BR0e5QVacD4xJZyVRZPOIWLnnmXbqceR0fT5hG9dzygaJk/doyvxevCxiUdXNh0H37AnYoWVvWl3vES7ZuLD1up06dmD17dtz7TJZff/2VI444giceeShsqA4nr6Qo6CMSKdrIkw/eyw033cyyPxbGdOxUKCkpYd26daxfH/m9Ua1aHk0aNigTqhvUymPBtx+wV/9jab37jlzXqGZe6cOnTvXc0oe/utVySh8mKhvw0BtIHZcAs4CBKaqbMcYYY0zGq9CrU1V9Gmd6rVIi8jFwKHC/qnpuwlPVs/1+LkhQFVPmvRvO4aKn3+HkA/bk/RvPJT8vh5aN6lOybhVZAa3SEVuuNxeW6xIOTtAN1zXci0gBPZQ+ffrw0EMPxXXsZPntt9849NBDueeWGzluwCFQ5Lby58V3rsKRorLncc9uezD0/HO46Ipr+fyrMeW6n6eD7OxsRo4cyRlnnMGMGTNo0KBBuTK+IJ2TV41N61YDTqAuLt7O5E/fZNInb3DRY68npGW6QX7w8iaoiL2BROR6YLmqvgzUA9amsH7GGGOMMRkt7Zp9VHUNkafVqlQ2vXM/z3w+gQfOHshJfbqXWx9zuIZyATtc1/Bg5RKlS5curFq1ihUrVtC0adOE7jseCxcupH///txw5aWcNviEMusCw2+yXXfFpRx42ECef/55LrjggpQe26sjjjiCQYMGccEFF/Duu++G/AJgz0OO5PMn/kuLz3ejRYfdmDvxW+o2aMDVj79C685dSsulc5jeVqJs3FYS9XYlmpY9pD8ExotIc2AAcLKI3KWq/iOEDwfeFpHzcWZY+DL11TTGGGOMyUwV3RW8Slu8eDE/Pngl439ZxJSFSzlgt7ZRbR+xWziE7Roe7uHJ5kJvDyArK4vevXvzww8/RPMUk2rs2LH06dOHay67iCHnnFmhdcnasp5qxVt49I4beeSRRyq0LpH83//9H4sWLWL48OHl1jWolUeDWnn0PuY0bnx7PAcdO5is7Vs58dLrueapN2ndeY+g3bz9u3gHhuoG+bmlDxMbVS3EGcBsEnCQqs4MCNWo6hpVPVRVD1TVi1XT8xsCY4wxxph0lHYt1lXB9u3bufbaa3llxHCa1q9DiSoPnjOQZvXLd9/2CdZqHUzQAc1CdA2PWoiQHrFOv4wlu0sB+++/PxMmTGDQoEHx1yUOqsqzzz7LrbfeykvPPM7BfQ+okHoEu4997+57sHTJYtasWUP9+vUroFaRVatWjTfeeIMDDjiAAw88kM6dO5cr06hmHtTMo1n/gfTqPzBoy3S4+6TDheiaeek7HVk6q4q9gYwxxhhjUsWCdQWYNm0aH77+MtMfvYaGteO7jzewS3hIsYbrGMJ0sJbz4onvsc8++3DbbbdFX4cEWrJkCRdccAH//vsvX3/8Dh3bt4tpP4kcMd1fTk4OPfbozISPXuPIsy9NyjESoVOnTtx9992ccsop/Pjjj1Rz56O+4aD2PD95xywEXgO1BWljjDHGmAoU7PY+67wWFesKniLTp0/nl19+YeXKlQy/+T+0alw/6lBdEqyrNx67hEOZrtlhBXTjDqV43aqgj1C6lvzF9OnT2bat3Cw+SaeqPPPMM/To0YOCggLGf/ZByFAdbIT0RI+YHoyvG37bljuzaPGfFP8+LSnHSZQLLriAtm3bctNNN5VZfn6v1gAhu3r7hOviXTMvu/RRbl1uVunDGGOMMcaYdGAt1km0detWJk2axB133MHChQvJy8vjj98XcVrfHrx4+Skx7TOaLuFA8HmuY+zS7b9fr/xDf22gZcM6zJkzhz333DPmOsRi3rx53HLLLYwbN452TeuVWZesoOxFsPvZx0+exiVnDYbNhaXd6P1t2bKFa6+9loYNG1ZoDwAR4bnnnqN79+4cdthh9O/fv3Td+b1a8/asZZ5bp8O1SluANsYYY4wx6c6uWBNs6dKl3HPPPXTp0oU6depw8cUXc9ppp7Fw4UJm3nMBa167m6cuOoGm9Won9LiBrdY+0QbhUPuI1BrtXw//R6C9O+7C+KfujLtO0VBVpk6dSrNmzWjXtF5KWp/DCTdI3KIlf7Jh0ya6tdkxcnrxL2N3rF+0iP3335+lS5fyxBNPsHjx4nL7SKWGDRvy8ssvc9ppp/Hiiy/iP97VSV2bl/4crHU61lbp/BwpfRhjjDHGGJMOLFgn0IQJE+jatStLlixhxIgRbNq0iZ9//plzzz2XbR86Iz1nZcV/yr12CfeJNVx7CdORgjQ49fU9erZuwpSFS9n0zv0x1SkaW7Zs4bXXXqNPnz7cfvvt3HHV0IQE6Ugjqscz4vqXY77l0N49ykxjVbxuFZu/f5tHH32UXr16ceaZZ/LBBx8wdOhQ7rjjjrifT7wOOuggxowZw+OPP87AgQNZvnx56brDOu7kKUz7B+nAMO0fpC1MG2OMMcaYdGRdwROkaOJ7PHjzg9x23iAu7L83rP+FbWN+YWuEYLqt0OmWnVsnAaN2hxG2a3iQcuGECtBlyoTYT8/2LXli1Hi2rl3HthHDqHveXRH3FY1Fixbx5ZdfMmfOHN5880169OjBlVdeyVG9OpOTE9vbPdFzegflds//fuos1q7fyLRf5rNn5/aUFK5m+rxFXHzfM9SpUYOxT95Kh1bN2TbpfS7dqwkdz3iYu4/sTtPjL0t+HcPo2rUrP/74I3fddRc9evTgxx9/pGXLlgDs3ao+Py1ZEzRMhxIuQOdb13BjjDHGGJNmLFgnQPEvYykuLmbCrLk8cPnZnrfzhWr/n70G7FD3WkcaJTzeruGRQnWoQO3TtXUzGtaozvNjpzDk4L3jqkswJ554Iu3atWPPPffkp59+YpdddgFg+7J5Ue8r6YE6yL3u/3f1RYx4bxQnXn4LmzZtpm2Lpvyx/B/uvvh0Th/QFxEpfQ3q1arBrs0bs+DPFTQtt6fUy8vL44477qBmzZoMHjyYcePGkZvrtFbv3ao+v6xwnm+oQB0qTFdUkN5eomwsKo56uxIbQNMYY4wx6chG/k4qC9Zx+mfaN7zw2ls88/oHdGjVnKYN68HmDRG38w/VwZZ7CdixhutYxNNK7eN7bv875ygO+7+X6L9HO7olonKuadOmsWrVKn766ae4utwnNVBHGDhu56aNGXb60dx82lEsX7mG+UuWsfuurWhUrw4l69cS+NHXtvlOLFz2DxUzE3dw1157Ld999x033ngjDz74YOnyUPdLBxMuTOeVFMVfSWOMMcYYYxLIgnUMVqxYwWeffcao995izPgJHF3Qm9fvupqeXZzpm0oibB8qVAcrEylghwvXQNwBO5GB2qd904ZcOWA/LntpFGOveyIh950DvPTSS5xzzjkx7y+mQB3HCOvB+HoUiAjNGzegeeMGQOjXoV2LJixa9k9C6xCvrKwsXn75Zdq3b89VV11F8+bNy5WJNlBbmDbGGGOMMenMblb0aObMmdx666307NmTTp06Meq9txhwSF9+HfMBL957Y2mojsRLqI62fLhg6yUYx7qtb0CycELV/+JDe7GlaDvDhw+PtXrlqCq1atWKejsvg4qV4XGeb6/CjboeblA4gF2b78SCP/9OSD0SqWHDhvTt25dx48aVLgs1AFl+blbpw19eSVHpw58UbSx9GGOMMcZUOiLBHyatWbCOoKSkhDvuuIMBAwawadMmHnzwQf6aN5M3hz/GWYOPp3HD+qG3DQhK0YbqeLcrrUeEcBZLeS+BOly9t6/byMPHH8TNN99MYZzPz6dz5878+uuvUW3jOVAnOExD+FHXvb5mvTq1ZcLPC8pMc5Uu+vbty9ixY4OuszBtjDHGGGMqE+sKHsbatWs544wzWLNmTek8yFs3rHMu7ou3RLWveMPxtsLCsN3CQ3UJL1MmzL3XXoN3rC3UPlvX7JjuqkOTBuy11158++23HHPMMZ6OH07nzp159dVXPZWNKlAniNeB46L5EqRDy6aowvz58+nYsWOMNUuOgoICnnnmmTLLounqHS5EV8T848YYY4wxxoRSZYP1lm3FpaMU+2zetInH/u9Oli/9g+rV85k+5UcOP+Ionn7hZWrVrM76TZvJi+FY8YZqr7yG63j2H040odqnf//+fPnllwkL1r/++iuqWmYeaH+pCNSxjrweVa8Cv2McvHtbPr3vOjq+8FFMx02WPfbYg3/++Yfly5fTrFmzoKE6mkBdmcO0iDQB3lXVA9zfc4BF7gPgMlWdHWLbEUAXYJSqJnb+OmOMMcYY44l1BQe2bdvGhHHfcNJhfVm3ZjXHnXgy/Q4bwMhX3+Se+x+kbs3qnvflC0eRQmhM9Yzzfut4JCNUA3RZ/BOTJ0+OuV7+dtppJ7Zt28a6devKrYvqPuooQrX//dHhunaH47Xrvf/D3yHdOvDF9Llseuf+qI+dTNnZ2fTp04fvvvuu3Lpg3b0heKjO2rK+sofq+sBLQE2/xV2BN1S1wH2ECtXHA9mq2htoKyLtk19jY4wxxhgTqMq2WK9Y9hfDrryENatXMW3yRFq2asPQq6/niGNPKJ0WKHCQpXL3fQYEtWDhKFzg9A+b1erX9lTvSF3CwVvLdTSSFaoBdmveiJ9nfkBRURF5ebH0B9hBRGjQoAFr166lXr16se3EQ6iOdy5wn0SMuA5w2J6duOPNL3hz/HROrTuC6v3PK103d+5crrrqKrZv346q0q9fP66//vp4qh2VAw44gO+//57BgwcDoUf3DhWog5ZN9vziqVcMDAb8uxzsCxwlIgcBs4ELVXV7kG0LgLfdn78E+gALkldVY4wxxhgTTJUN1jm5uey59z7UqVuPW+97hJ2aNottR5sLywUtXxjyD5zhwqVvfbqF60S0fkd63lnrN9Oybi2+vvAEjnjx47iPV79+fdauXRv9hhkYqH1q51fjtavP4Mg7htN3t11p8eWOcD116lS2bNnCDTfcwEcffcS8efNirXZMcnNzS6c/i7fbd6oD9dbtJazevC3q7baXKCIyBBjit3i4qg4HEJFnAf8b4r9R1TsCbl/4CeinqstF5GXgCCDYH0hN4C/359XAXlFX2BhjjDEm1UKNcp6GA/J6VWWDdaPGOzHo1DOj3i5UEAjXBTxSuAws5yVgJztcew12XlvkA21ZtWO73Zs0ZPaKVex+4zm0uvdF75UMsHHjRv7666/oW6sjhOp4A3Ws905HY4/WzWhQqwbrNm2mBbDFDderV6+mS5cu9O/fnw8++IDOnTuzbt06SkpKqF8/9Ij2ibJy5UoaNWoUdJ3XVupMbKF2Q3TQueRU9UIPu5ilqlvdn6cAobp4bwDy3Z9rYbf3GGOMMcZUCLsIi0HWlvURL/Z9gdNrqPYXyzaheJlrOtbyiRqU7ZB2O/P6jHmUqLLkxnNi3s/LL7/M/vvvT5s2bSIX9jB9Vrz3TEczzVm0r1MwqzdsokHtmqX72fLlCFatWkWDBg0AWLduHbfeeiutWrWiZcuW9OvXj4kTJ8Z1zEhWrVoVNFgHhupg91GHvS8+wVOfpaFXRKSbiGQDxwIzQ5SbitP9G6Ab8Efyq2aMMcYYYwJZsPYo6L2hAd3Ag3UBj9XWNesjBuxojhMpuEUb7OK5r9q/tRrgiE5tEBE+/uV3tq5dz4KhJ3iuh09JSQmPPPIIV111VfiCHgNZNIE6liBdum0CAjU4z3/txi00qJVfZvkfk8aw0047AfD666+zadMm1q1bx5o1azj55JM5+eSTOeKII1i+fHncdQhm5cqVNGy4o9dEsLmoo2qlrvyB2ucO4BVgBjBRVb8WkS4iEjjq94fAGSLyMHASMCqltTTGGGOMMYAF65iM+W4CnQ46lka9j+bDsZOcQBXmvuotqwrDPsJJZLiG8qNLxxLsEhmqAbJEuPmgntz/7RS2bi+Oqi4+06ZNIzs7mwMOOCB4gSgCmZdQHWuQLt0+QYHaZ+v2YnKys1i/eWvp/ou2bWfUpBn0q7OBLV+OKPMo/vZlTm8l/PzKfbRq1Yrbb789YXXx599i7aXrd8hW6ioQqFW1wO/nOaraVVX3UNWb3WW/qOqwgG0KcQYwmwQcpKrlh8R3icgIEZkoIsNCrK8rIp+JyJci8oGIxDeaoDHGGGNMFVJlg3X13Gy6NK0T9NG6YS1aN6zFTnVrslPdmtSsXo1ps3/hwcf+x4mnnsl5V97AYzddyuB++/PbX3+X7tO/+7d/qI7ES5l0kaw5ufdp1ZQujevxxNRfY9p+8+bNNGzYMOj81cW/jPW0Dy9dv+MJ06X7iCFQbyssDHvu8/NyOa3vXvzfe2NKl302eRadWjWnTdPGQbfJql2PvNxcbjlmH9555x2WLl0adb3CWbNmDQsXLqRx48YUrV5W/vhep9Cq5IE6Xqq6RlXfVtUVocp4nJbrNOBhVe0PrAAOT06NjTHGGGMqnyobrEMpKSnhq6++4oQTTuDAAw+ka9euNGnShPPPP581a9Yw+MxzmTbqDQ7r04vlK9fQqp5zT2vgPdVeWqP9hSub6FbrWHk5TrSt1f7uOHBPPpq/hB/+/DtsuWgUTXzPU7lkBup4ewd4fX1vPKEfw7+YyNZt28mq25DXvp7Iaf16R9yuUb06nD3gAO67776o6hbOrFmz2HvvvTn++ONp13pnT9uEbKk2iVBA+Wm5ylDVp1T1K/fXxsA/gWVEZIiITBGRKf/++2+y6mqMMcYYk3Gq7Kjg/n799Vdef/11PvzwQxYuXEjbtm256qqraN++PXXr1qVx48Y0b968zDZFE99j0dK/2KXOvkFDdSy2rCqkesPwI32H4mWU8HTXML86N+3XlaemzuXsKLdV1XKt1b5QXbxuFdlBRkf32uU7FvF2847ly5KfFiylZ7uWVMvNYeW69Xw3cy7PX3Nu0LJZteuV+f2KU46m+6lXcNFFF7H77rvHUmXAeR1efvllrrnmGh577DEGDTzScxfwciIMLGei4nlaLhHpDdRX1UmB6/xHO+/Zs2fmzodhjDHGVFbBprHK4CmsMklGBGsRGQB0Bsap6tRE7PPPP/9k8ODB/PLLL6xevZqTTz6Z559/nt13352aNWtG3D6v9yCKi/9DXk42EDpUb11bNkBUq+dtrupA0cxznQypahU/pE1zbh8/g4ULF9KuXTvP2wUG68CW6miDWCyDkCVCpPMc7guUF77+kXP67QPAO2N/4vBeXalTMz9o2UBNGtTjgcvP5vijBjBx6owyA455NXHiRK666iqKior4+uuv6bRrG0/bpWuoLipR1m3dHvV2xen5n5enablEpAHwBDAoRfUyxhhjTDTSbf7ndKtPBarwruAi0kRExvv9HmyAnd2Bx4H9wuxnpIjc4/58m4jcFu64ubm5HHfccTz//PMsWbKEhx56iH322cdTqAZnUKa/N2yhbo3qbF2zvlzX761r15cL1eGW+8Rzv3Wywq/X/cYzTZjvnORlZzGwfUtGjhzpedtt27axatWq0mC95csRcd0H7WXbeLp3BxNNl+9Qpi5cysFdnVtnX/16AqcfGvzPJbC12uf0AQUcffD+tNy5BW3btuWYY47hm2++iXjcP/74g8GDB3PSSSdx8cUX89NPP5WGaq9zVXtlLdUxizgtlztY2TvAjaq6OHVVM8YYY4zJfBUarEWkPvASTjfFcAPsfAtcyY57BEO5QESqezl2kyZNOPnkk9lnn33Izs6Ouu4PP/wwJ510Eu0vuqfM8kjB2Uu5UOE6kfNbe5Wqlmp/x3dqw8svv0xJSUnEsvPnz6dx48Zceuml7LXXXmz5ckTpuljCdaRtEj2adyyBOlT5lo3rs3z1On7982+Wr1rLQd07R12f+6+5iDU/juLTJ25j0KBBnHbaadx1110hX4uff/6Z3r17s9tuuzFv3jzOOOMMtm1y3qeJ7gJuoTouH1J2Wq6fg0zddR5OF/GbRWSsiAxOcR2NMcYYYzJWRbdYFwODAd+VdAFBBthR1Smq+oCqRhrVag7OyLZJN3r0aM444wwAdrrmMc+BOlAs24STyCCcyH1F0xLfqWFd8jasZfLkyWHLqSqXXXYZt9xyC8uWLePWXo2ZMXN2mTJeBx2LVC4dAnUkLRvVY/E/a/ho+m8MOrAn2dnl/7xDtVYDpfeh5+Rk03GXVpzWsxVTpkzhiy++4NRTTy1Xfu7cuRx66KE89NBD3HLLLdSoUYOtG0LO9uSNheqkCDIt18wgU3c9rar1VbXAfbxVEXU1xhhjqgSR4A+TsSo0WKtqYcC8q4ED7DSJcpdPAheGWpmoEW1XrlzJokWL2HvvvQFYMPSEmPcFwcN1RbdaJzr0RTso2+FtW/DcRWeGLfPRRx/x119/cdlll7Hpnft5b8Is9rv+ce4b+S4acF+HLzT7zz/tZS7qRAdqSE4vAFVl1h/LaN+8MSJQs3q1hOy3adHfnHrqqRQVFZVZPn/+fPr168e9995bGrrjDtUmqbxMy2WMMcYYY2JT0S3WgTwNsBPGCmAuTstMOao6XFV7qmrPxo2Dz+3rxdixY+nTpw+5ublxh+pkiDe4VUT378BB3Q5vuzNf/PYX8y8KPYbShg0b2L59O0ufvJ5thYV8PXM+Nww6mE9/+pnTbnuczSvLdnCIpmt4IgO1r3U6Ga3UPrMXLycnO4suLZvQpH5dpi9YzHcz5/LHih1fIHlprS4jvw6qylOPPsQll1xSuvjdd9+lT58+3HnnnZx11llA+VAdczdwY4wxxhhjMlC6BeuIA+x48AjQN1EVCub999/n8MMPZ+ZJA0KW2bRqY7lHVRep1do/XHdoUIe8nCxm/bOm3JcXv/32G7fddhsPPvggK/9aytzlKykpUcbMmM+ZB+3NF7ddyMatRdz33piow3E8gTowQCcrSAcbFXzUlF84okcXRITeu7UjJzuLu179mH0vvoPlq9aG3V/QUO1SVf5ZtYaaRWvYsmULl156Kddddx2jRo3inHPOARLYUm3dwI0xxhhj0o91W/ck3YL1h5QdYGdUtDtQ1enAuATXq9SyZcv4/PPP6T7mIyB4gE6HEB1roKuI1mp/vnAtIgzq2IZ7J8yiKL8aS250QlxRURGHHHIIq1ev5o792jH7v+eyf4dWzFq6gno1q9OsWjbZWzbz1EUnMOLrH5m+6C9PQTmaQJ2qAB2N73/9nYO7tiOrbkM6t27Oe3f8hy8fuI7BB+3D8E/HRr/DfCe8Z2VlcftVQ/nPzXfSe+8erFixgmnTppXeBlG0elkCn4UxxhhjTCVigbRKSYtgraoF7r+BA+x4bgpT1bNV9Xvf/lT1tsTXFJ555hkObVCbOnm5ydi9J17vs07UaNOJ5OVe62r1alOtXm0uKehBl+aNOOOtryjcUsSSG8/h5Zdfpn379gxrJezbtgXZWVlsXbOeL376lYN3a1u6j0bZcPfpR3LF8x+gquWmx4p2uqx0CdCh/LxkBSBsKdpWZvmlx/VjxKhxbM0LPp91qC7g/s4541Qa1q/HOacM4rVnH6NevXpA6FBt3cCNMcYYY0xVkxbB2l86D7CzdetWnrr/Pk5p1yolx4tnTmufdAyCXgcyyxLhrv770rVpQ05/60u+/e1Pbr7yP1zUJLfcuRm/cCl9WjUts+zE7u0o3LSFcT//FnNd0zlM+5SUlLClaBvnPPY6J9z6BJu2bC1d137npuzZvjWfjp/ibWf55V+b7OxsPnllOBeffRoiQtHqZWVCdbAgHbUQ3cArUtH2Egq3bIv6UVyikXdujDHGGGMqlbQL1unspZdeon2tWjQrzmHLmi1Rb19RXcS9BMN0DY8iwu2H7kPPFjtx5afjubt/b/Zu6QwW7x+us0XIyZIyrflZWcKlh/bi4ffGRH3cdA3Uwe6vzsrK4ogeXWhavw5N6tdl0K1PsHnrjlG8+3TtwI8/zy+3Xbh7q320Wk1P9UpIuDbGGGOMMSZDWbD2aNu2bfzf//0f5+zSOuZ91GjoLaRkqmr1a0cu5Ipm+i0R4ZZ+vZh86Un071C2t4AvXLdsUIelbqj2D9eD992dX/76h+k/L4h4nHTv7h3OFQMPZNhZxzH86nNoUr8up9/9DNu2bwegZ4ddmPqrh1b7gNbqwFBdUj386ytFG60buDHGGGOMqZIsWHv0xhtv0Lp1a7rVqwtA9frVk37MaOd+TgfV6tf2HLCjfX552dlBl29ZVcjO9euwdM2OQOwL19Vyc+jUvDF/rFwbctCxTA3T/rq2ac6JBb3Izs7iuWvOIUuyuPaZtwDYq0Nr5v7xJ99N/7nMNtldCsjuUuD5GIEBOVnbGGOMMcaYKixDB32zYO1BcXEx99xzD8OGDYt5H5W9tTpQNOE6EV8gtKxfu7TF2scXrgs3b6VRrRpxHyNT/H979x0nVXX/f/z12QLsLm2XLlYsKFFBJCqiggUUu6igUWJLMBqTWKKoscVG7IVEIz+JGmMCRAN2ghpDNMI3QgSsWGJFUcpSF5bdnfP7Y2aW2al3ZqfuvJ+PxzzcnVvm3DPXYd/zOffc8rIyHrjkLKb9Yz4bN9fTpaqSP998Gaf/6i7+vfh9ANoN3Xp/8NIBI6JeW50vvAxZFxERERHJJQVrD6ZNm0Z1dTWHHXYYkHy1OlGoDr1/c1uSqaHh0fQuLePDb1fjXMuJo+pr1/PJ8lXs3KumVfsvND26duLgvfpzzdSncM5x6JC9ePT6nzPmit+w2+mXccYZZ1BbW9u8fulOg9PehmjV6qjDwPNw4jIRERERkWQoWCewYcMGrrzySu68804sT4YgJBNYvYo2KVY6ZHJoeKhB23THNTbxzJKW11K//P5nAHRqbEp6n/W165N+5JMpvzyHBUs/5fpHZgIwcv9BrFqzjpdeeomuXbsyatSomOHa66RlIiIiIiKiYJ3QpEmTGD58OMOGDUtp+2IbAh6L14Cd6tDwspISbhx1ADc8+zqLvvyWKa8t4gdTn2bi315l6g+PxsziBuF0heR8CtrVnap46safMfWFuXz2zXe0G3oyJSUl7Lrrrvz2t79l2LBh7LfffjzzzDPN26Srcp3ua6s1HFxERDKuQK/rFCkqefz/aVmuG5DPPvnkEx566CEWL14MwOKxo5MaBu4lVLfVYeCxtK/u5ClwBsN1Mvfy3m+7XhyyYx8m/OlFDt51O8buuwdH7L4jHTu0i7lNpsNv6P7TMdKgYd26mKMLfGtXURIWQHt27cyIQXvwxroKdgt53sy45557OOaYYxg/fjwNDQ2cfLL/uuvSnQbT+PXSqK9Rsnl9wtnBY4XqdM4G7pzjpqkz0rY/EREREZHWULCO49JLL+WXv/wlffv2ZfHY0Ult29pKdTpnBPc6zLu8c+eszI7tNVyDvx+SCdf3nZnc+5RNzTOVJxGwF/xvGRs2b6FrZQd6d+1Iz84dk37dbxrL2XnnnSOeNzNGjhzJ7NmzOfLII+nQoQPHHHNMwv2lUo1OR6gu7dKNprWraGxs4ud3Pcx/P/BwC7FW2NzYxMqNWxKvGKbR5xKvJCIiIoUjVkXU6d982UrBOoZZs2bx7rvvMmPGjIyF6lSq1cmEslSumw5uk+mAHTwOr9XrZMJ1vksmYF/4h2epaFeOw/FN7QbWb65n2+5d2b5HNTv0qObIfXZn3B2PAVD319uj7uN///sfffr0ifkagwYN4m9/+xsnn3wy7777Lt26dWsOwum41jpuqE5y4rLFH37K+ZMepFdNV+a+uYjOGZobQERERERaqci+kFCwjuLTTz9lwoQJPP3003ww/sSktk1HqE5Htbq1k5FlM2C3Zmh4Id7rO6i+dn3CcL1L724cN7g/px+4NwB19Q18tXotyzY18Pl3q7nkiZcoH/o3xowZQ+WpV7B5ztSIfYwdO5Ybb7yRP/7xjzFfZ9iwYYwdO5YrrriCqVO37qO1ATtdw7+3bGnght89yiNPPsctF57Jj27+bd5MJigiIiIiomAdZvPmzZx66qlcffXVVN5zY1LbZjpUe61Wp3OG72wE7GSHhrclicL1hMOG8OP/9zTf27Yne2/fm8r25ezWpzvf69yZylOv4JC33uLII4+kW7duDB8+nA6jzosI15MmTWLPPfdk3rx5DB06NOZr3XzzzWyzzTbcfffdhA84t/qNSYfrhKHaY7W6btNmxl5yAwBvzXqEvsPHJNUOERERKSLRvnhvoxVSyS+aFTzMpZdeyo477siIf89OartMDv+G3ITq8P2GPtItE7cQKxTxvlQYMWAn7jzjSMbeN52Plq9qfr7y1CsA2GeffZg2bRpjx45lyZIlAHQYdV6LfVRVVXHssccyf/78uO3o3Lkzffr04dtvv4263Oo3eq5Ap6tS7fP5OOvKW6mq6MDMyTcrVIuIeJHHs+aKiLRVCtYhHnnkEV566SUublqTk2GmhVSNzUTILvZwHStgnzBkD64dM4KT7v4zn69cQ5fzbm6x/LDDDmPy5MkceeSRzeE5PFwPGDCA9957L2E7unbtyqpVq+KuEwzYscJzOmf/vu3hP/PNytU8ftuv6DDwiLTtV0REREQknRSsA1555RWuvPJK7ti5N53Ky5Patq1Xq728brqCdjGH63jOGDaQnx15ACdNeZF33nknYvnYsWOZOnUqxx13HDNnzgRahuvBgwczZ86cuKG5sbGRpUuXskuHzZ7bFRqyk6loexkG/vzcefzuiZlMv/t6KgaN9Nwmic7MpprZPDO7Js46vczstWy2S0RERKQtULAG3nvvPU4//XRuHbAjOyV5O6N0hepY1ep8D9XRtDZgK1xHd/7h3+fyEd/j0EMP5eGHH8aFXS909NFHM3v2bC666CJuu+02fD5fc7jef//9Oe200zjxxBPZvDl6cN64cSO+xgY6WqM/+CY5Y3cipTsN9v/gYb/zFr3Leb+6jacm38T2h56a1nYUIzMbA5Q654YC/cxs1yjrVAOPAa2fCl5ERPw0LF+kaBR9sP7222855phj+MVOvRnSoyapbTMdqr3Kp1AdSuE6/U4/cG+e//nJ3HfffVx55ZURy/fdd1/mz5/P008/zfHHH8+aNWual02aNInevXtz8cUXR913x2VvsduO2/Hf9z/a+mQwYKcSskO2DYbq5nAdxzsffcrJP7+WRyZdxYGnXZj860o0I4AZgZ/nAAdFWacJGAfEfLPNbIKZLTCzBStWrEh7I0VERAqOvjyRgKIO1nV1dRx//PEcWVnKsTv0TWrbbIRqL+EyX0N1OihcR9d/m+48+6NRPPbYYyxcuDBi+XbbbcfcuXPZdtttOeGEE6ivrwegpKSEyZMnM2PGDHw+X4tttsx7iqa1q9hlh7589tU30V/Ya8gOW6d0wIgWi0N/b2ho5Jl/vM419z3MCRdezU6Hj+PgMy7ijssv4NjzJ8Z/nQyrb/CxesOWpB9NvrycebQKWBb4eTXQK3wF59w659zaeDtxzk1xzg1xzg3p0aNHBpopIiIiUpiKNlhv2bKFgw8+mL4rvuYnA3ZJaluFau8KoY35IpkvEqo7VnD90fvxk5/8hKampojl5eXlPPDAA/Tq1YuzzjqrOUj37t2bXr16ccstt9DY2Aj4Q3XQntv34ffTn2HF6jXxGxAassMfIcJDdejziz/4mF2P+gF3PTKDduXlnDNmNC8/cjer5j/LDyfe4rkvxJMNQEXg544U8We/iIiISCYU7R9X77//PsMbN3Ldvt9LagbwTE9U1hZpSHhmnH7gXrRb9x0XXXQRmzZtilheUlLCH//4R5YtW8bEiVurv7Nnz2bu3LkMGzaMxX+5r8U2l48/kSG77cApv7iu1e2LFaqD/vrmJ5x29OHMffx+rrvwLI77/gB23r4v5Xse1urXlggL2Tr8eyDwWe6aIiKSQxq2W9z03ksGFW2w3qZDO87qv5PnUF3ZrcpzqPaiGKrV6aJwHZ2Z8cSFp/DtgrkMGTKERYsWRazToUMHnn76aZ577jlOPPFEzj33XG655Rb23HNPjttzW4644Bru/vPTzVXv0tJSbrngTL765lvmz5tP09r4t96KJTRUb968mSVLlkRMtvbaa68x6rRzaVq7qvl1EoVxSdksYLyZ3Q2MBd41s5vjbyIiIiIiXhVtsO5YXuZ53WQDdaaHgBci3YYrM6o7VvCH80/iF0N3YdSoUUybNi1inZqaGv7xj39w0kkncdBBBzFkyBBKSkr4/TOv8ruLz+L5uf/H8Zfdyqq1/vtol5WV8tNTR3P/tOcAmoOvl5Advl5TUxOnn346hxxyCPvvvz9PPvkkM2fO5OKLL2bx4sUMHTqUdkNPBmj+r6Sfc24d/gnM5gOHOucWO+ei3nbLOTcii00TERERaRO8p8siVrdqY1qr1ZtXrYsZrutr13sKkQ3r1hVU1bphXXpv3dTWtOaLAzNj3NC92LlXDedccQVjxoyhXbt2Ldbp06cPZ511Vovnhg8fzo/PGs+tPz6F979ZzdBzJzLtlssYvPvOnHPc4Vzz4BM0NDZSXrb1Y8JrBXvLvKcoP2AMF110EevXr2f58uW8+OKL3HvvvVRVVXHIIYfw2muv0bGj//Z2CtWZ55yrZevM4CIi+SHayEGXl5NAiojEpWDtUTLhun7N+qxcYx0Mq4UUsFujfXUn6mvX57oZaZXOSvyQfn3ZuWMZTzzxBOecc07C9U844QR2fv0NRo04mKmXn8f399iF4y69hWEDd2fVmvWUWAllpaUpt+fF+6/n1Vdf5T//+Q8dOnTgpJNO4qSTTkp5fyIiIiIZE+vyUH3RIx4V7FBwMxttZpea2b7Zes26VRuz8jrJhseGdeuKpiLcloaEZ+JYLh49lDuvjby/dSx77rknjzwxjfPveYzDvr83c6fcwqmHD+OGCafz9rT7kprYL9zSz5cxYrdt6FwkX/ykysy6mNmLZjbHzGaaWbvA81PNbJ6ZRR2yHbK9p/VEREREJHMKJlhH+eNxT+B+4MBstsNruK5fEz8cb16V/iBcLAG7LYTrTB3DIbvvyNq6Tcy/8XzP2xx55JEcd/JYfnHX/2OnPj059YhhHLzPALbvndp9in3r1+Bbv4ZPP/+S7Xt1Y/OcqSntp4icAdztnBsFLAeOMrMxQKlzbijQz8x2jbah1/VEREREJLMKIljH+OPxVeAScnDNYDYq160Z8pxvATsTbWlf3akgA3am211SYpz0/QH87c33WDvVewHz9ttv55v6Ug7+8dW8+d5HSb1mMEgHH0HLVtTSt3s1QEGG600NTXy3vj7pR0OTDzObYGYLQh4TYr2Oc+4B59xLgV97AN/hn2gs+Nk2h623ygrndT0RERERyaCCCNZE+ePRObfAOXeHc+7b3DUrvkRV60wLBux8CtnpVijhOptfBJx6wJ489q+3+ODrFZ7DdVVVFa+++iq/uPp6Tpl4Gw8+9WLc9aMF6XA9qzvzbe3a5t/r/nq7p7a0Bc65Kc65ISGPKcFlZvaQmf0z5HFd4PmhQLVzbj5QBSwLbLIa6BXjpbyuJyIiIiIZVCiTl4X/8Tg4lZ0EqkYTAPpUdmhVg0Kr1vEmNQuG62iTmQWHg8ebITyotaHMa7hO10Ro2Qzz+T6pWbbD/8Dte3PTqYdz/J1PMOMX4xjucTsz48wzz+S+++6jT7fqiOXxQnQ0u2/fh7f/9yW+FO+F3VY55yLG6ZtZDTAZCE6PvgGoCPzckdhfgnpdT0TETxM0pZ/6VEQonGCdlj8eA1WjKQB7dO7kNtduTrhNh+rEAdxLyG5NwIb0hux4CrW6nY/hOpfV9HFD96KyfTmn3DuNWUeey7BhwzxtN3v2bOrr6zll4m00/t/MpMN0qP7dqnj0+U9wzrVqErS2LjBZ2V+Bq5xznweeXoh/WPd8YCCwNMbmXtcTEWlbFGZFJM8USnUj+Mcj+P94/CxbL7y5dnOLRyJ1qzbGvQa7fs36mEPEN69a52lSs/ra9c0P2SqfhoXnQ1uOG7w7D513PCeddBK1tbWetnn00Ufp168fGzduTPne0r61q/CtXcWBu+/I5i0NvLjw/ZT2U0TOwz8K51eBoeHjgFnAeDO7GxgLPG9mA8zs5rBtI9bLXrNFRERyxCzyIZJjhRKsZ5Enfzx6DdnBgB0rZHsJ2MmGbAXt3AfafJtU7fA9d+bEE09k0qRJntafPHky1dXV7L777hx//PHU7nW059cKBuqgstJSbv3hMVz9+PM0NDYl3fZi4Zx70DlX7ZwbEXhMd86twz+3xHzgUOfcWufce865a8K2jVgv2+0XERHJCIVnKTAFEazz9Y/HZEN2NPECNnivYjfvT0E7J8E23wJ1qBtuuIGpU6fyxRdfJFy3Z8+ePProo7zwwgs0NDQwceJEOow6L+F2sa6jHjWoP9v3rOb8B/7KV19+nXTbi5lzrtY5N8M5tzwd64mIiIhI5hREsIb0//Hoa3Jsqt3s6eGFl5CdzYDdvN+woF0sYTtbITefA3XQNttswwUXXMB1113neZuBAwdyzz338K9//QsgZrgOr1KHMzOmnnc8PSrbccB1U1i2bFnMdUUkT6hKJCIikrSCCda5lGzQzkbATiVkN79GkYTtTIXe4H7zPVCHuuKKK3jxxRdZsmSJ52369+9PeXk5s2fPBiLDtZfZvhvWraNrZQduHnsEe23Xi/feey+5houIiEgkfQEmkncKZVbwvBIeritizBweGq6jzS4eDNfRZhKPN4t48/5DwnW8GcW98BKuCylIttUvC1Kxduo1dDnvZq6//nouvPBC5s6dS2lpacLtzIzJkydz4YUXMn36dHbYYQd6jDyX+pf+kDBUR5tdvm9NZz544j5GjhyZ8rFkU31DE6s21Ce9XZNPM9KKiIiIFJuiDda+Rh+bVtYlXK+ie2XCdUKDdioh20vABu8hG1oftKNJNqxmM4hnM0hn69Zn6bR26jX85Cc3Mm3aNCZPnszFF1/sabujjjqK8ePH85Of/ITPP/+cnj17Mm/ePMrnPBRzm1i3bOtb04llq9fx3Z2/oOcv70vlMEREJJN0CysRkZQVbbD2Klr4jhe2kwnZsQI2pF7Fbn6NKEPFMxG24ymGqnEhhez1j1zHH/7wBw444AAOO+ww9t57b0/b/frXv+bXv/41ABdddBHnnHMOTz31FJuevCNi3Xj3Qd+2ujOvLP6INXV7QCBcO+dwzlFSoqtSRERERKRw6a/ZFGxaWdf8iLteguuy412L7eU67HjXYkd9vZDrs1t7nbZEKoRr1nvMfZTf/e53jB49mk8++STp7e+66y4+//xzpk+fTuWpVzQ/37BuXdxQDTCwRzWfrVrLvrc+wu7XT2Hb6k5UVFSwyy67sHLlSgC2bNnCSy+9xNVXX82iRYuSbp+ISM7omtf0U5+mJlq/qe9EMq5oK9Y+n2NdXUPC9TpXlsddHh6uY1Wz41WyY1WwIf4wcfA+VDyWeOE62xXutiQ8XOdLNbu+dj2H1r7Btddey8iRI3n99dfZZpttPG/fvn17ttlmG9q1awdA5alXUPfX2xO+JsCAPt15+eLTcc6xcuMmNjc00q2qggfWdGTMmDH07t2bOXPmsMcee3DggQcyatQoTj31VL73ve/Rs2dPjjrqKDp27Jj6wYuIiIiIZEjRBmuvwsN3MkE7UchOZ8AGIirYqQTtFu1JUNFW8PYuVhU7XuDOZOV7zIb3qf3xjzn66KOZP38+HTpEv2whmoULF/Lb3/62+ffKU69g7dRroq4b7RjMjB4dK/3n1/pNnM9G2g8fzk477cTkyZPp1asXAJdffjkPPfQQS5Ys4e233+bJJ59k2rRpAHz66ac8/PDDyRyyiIiIiEjGKFgnKZmgnShkZypgB6U7aIdT8G69XA4bP7fsGxbusgtXXXUV99xzj6dtvv76axoaGth+++1bPN/lvJsjwnW8Yws9d8pLS/hx41fw0VfU3/saH4Wctz8AMNg8oCfHzXiOn+67B4u/W827K9dwzM7bemqziEibpInGRETyStEG6wafY0V9U9x1erRPfEuiYND2WslOd8AGbyEbIoM2pD9sh/J6DbcCeG6YGTfvVcPIh5+kb9++XHzxxZSVxf9IWLBgAUOGDMGi/EEXDNdeA3W4eHMGdCgrZdKhQ/j9fz9gTP8d+P1RB9K+rJQn3v1f3PbKVmY2FRgAPO+cuznVdUREREQkUtEGay9Cg3eikB1ayfZSxY4VsKPNJB4vYENqITso0QRomQzeQap8505NVQVPnnEYVz7/PNOmTePhhx9m0KBBMdf/+uuv2WGHHWIu73LezXx35y8ink/0HnuZiG9In+48fMxBCddLl4ZGH+s3bkl6u3y8j7WZjQFKnXNDzewPZrarc+6jZNcJtXTpUkaMGNHiubFjx3LhhRdSV1fH0UcfHbHN2Wefzdlnn83KlSs55ZRTIpZfcMEFjBs3ji+//JLx48dHLL/ssss47rjjWLp0Keeff37E8muuuYYjjjiCRYsWRb2l3K233sqBBx7IG2+8wdVXXx2x/N5772XQoEG8/PLL3Hxz5PcKDz30EP379+fZZ5/lrrvuilj++OOPs9122zF9+nQefPDBiOVPPvkk3bt359FHH+XRRx+NWP7CCy9QWVnJAw88wIwZMyKW//Of/wTgzjvv5LnnnmuxrKKighdffBGAm266iVdeeaXF8m7duvHUU08BcNVVVzFv3rwWy7fddlv+9Kc/AXAxsCjstXebMIEpU6YAMGHCBD788MMWywcNGsS9994LwJlnnslXX33VYvnQoUOZNGkSACeffDKrVq1qsfzwww/n2muvBWD06NFs2rSpxfJjjz2WX/7ylwAR5x0kee716BGx/AJgnHOtO/fw99vFoQsCbc34uQdsB0wHHozSP08C3cF/7kUshReASuABIPLMg38G/puRcw/4U+DniwmceyHHsBswJfDzBODDsOMbBNwb+PlM4Kuw5RHnXtixHQ5cG/h5NLApbPmxwC8DP8c894A6IOLMGzGCs4GzgZXAKWHLAC6YO5dxwJdA85k3fHjzagnPPdh67kVp363AgcAbwNVRluf0c2/u3OjnXsjx/zPw3zuB56DFuVEBvBj4+SbglbDj6wY8Ffg54bl38cWRn3uEnXthywcRdu6FLR961VVMCvx8MrQ890aMiH3uBY6jxbkXtgxgLLQ898KO/2xCzr0o7/0F4D/3Yn3uAcfh//c+8swLO/eiLL/1jTe2nnvhC0eM4F78ffgycHPYMoCHgP7As8BdYcsg7HMvbBlk9t/cWBSsPQqG7HRVsWMF7FjVa0gcsIGImcSTDdrhkpl5PFMhXME7s3bq3pVpR3fh+W6DGDVqFOeccw7XX389lZWRX/7U19fTvn37uPvr+cv7msN1OgK1pMUItv7NMgc4CAgPzQnXMbMJ+P++SHgepMvcudF/Hj685e+hy996K3J9gGHDto6SDV8WauTIyOeCf+fFGn07fDiEXSER0dYoea7FcVRVxV4W/trRjj+4/MYbox+fWfzj/1PwL8xf/AJizMof7fjj9U1w2W9+439EWz53rv9x3XXRlwXbe/nl0ZcFl//0p9GXBZefcw6sWEGLP9qbXXABEPs9vOwy/3933z32MeKcv9+ifKkzbFjs7ebOhX32ib48eBzhrzt8OC2P4/HHYbvtYPp0ePDByGN88kno3h0efTT68b/wgv+/v/sdRPkDE2Kf+0cdFXu5l3ODM87YevJdfHHLc2/4cNhtNwh8qcOECRD8Uie4g0GDIPClDmeeCV+Fx5tYLxxw+OEQ+FKHo46CsC91OPbYmMcAMHYs/ve+rg6ifKnDI4/A2WfDypUQ5QtFpk2DcePgyy8hLNwE/9+I2vzgD9dcA0cc0fLcCz3GW2+FAw+EN96Aq6+O2F/w3HvppeifC7vv7j+844+PXBYU73MxruHD/edeZSU88EDkuTd8OATCDXfeCaHhZvhwqKiAwJc63HQTBL/UCb5wt24Q+FKHq66KbNS224Z88EVpcOi59+Mfbz33goKFCOdin3vBD92TT4awLxS58cat597o0S3PveHD/ede4AvFFqEx2M6xY+HCCyPPveDys8+OPPdCj/GCC7aee9GOP/jBF20Z+M898P+jG+VzD/Aff+Dci/DWW/4+fPllCP9SZ/hweOgh6N8fnn0WQr/UCbYl/HMvD5gr0mtxti/t4K7oGLvy5oWXkB0UL2THmuQs1n2wIX64TqS1Ybu1slEFj0UhPD43/mp+/vOf89577/HKK6/Qs2fPFstvv/12VqxYwR13RN7DOtwXV50Tc1kqgTra7ecG/XX2QufckKR3loCZ7dvngGMX7Db2l4lXDrPw7gms/3JpXt3XJDDE+37n3GIzGwUMds79Jtl1Qg0ZMsQtWLAgsw0n/mWkiS4xjbY8E8u8tCeflkH8Y4ynLfRbPPnSb8Hl+XQZdab+fysUuXgvMtFvmfh/I5/OU5FMM7OYf3sWbcW6ycHqLfGvsa5pFz84p6uK3ZrqNSQfsmPdHztbgTteqMp06Fb1O776e69k+vTp3HDDDRx66KH84x//aJ6lG7xVrIO2n/RIRLhOJlDHOk8lJRvwj5oD6AiUpLiOiIhICwrQIn5FG6y9CAZvrwEbvF2LHat6vWllXVKTmwWFhmxIvZrtNchkMoDHCl7ZqnIX2329ox3vF1edww2THsHMOPTQQ5k5cyZVVVU0NDSwfPnyFkE7kWC49hqoFaYzZiH+od3zgYHA0hTXERGJqq2Hq7Z+fCLSegrWHoRWttNRxU6leg2JA3ZQuoJ2LKmEn0xc653tIeW5DN1eZ1hPly+uOodzgI09Kjh430GUlpRQVlJCWYnhHNxyyy107NiRTp060bFjR4YNG8bEiRPp169fxL62n/QIH10Q5bqyAIXprJgFvGZm2+CfI+U0M7vZOXdNnHUOyHorRUQko1L9gkBfLIgkpmCdpNVbmhKGa/AesJOtXoP3gB0UHrSD0h2440kUnlIJ3vkwo3lQtoNvtlx04N5cdODezb//+uX/45v1dcw57mBqJt7D+vXrWbduHU899RT77bcfo0eP5sQTT+SII46gS5cuUfe5ceUGgKi37JLMcM6tM7MRwEjgdufccmBxgnXWZrmZIiIiIgWraIN1Yyu+evM6RBwSB+xE1etY4Rq2BmzwHrJDxQrcQfkSvFOtdudT8G4LXv3kK2Z/+AWzxhyGbdhE7bU/YdcHnwRg4MCBXHbZZUyZMoVbb72Vu+66izfeeAOAxWNHt9jP5Hc+5MlPvmJIzxr261nDfj27sVOnKs9BO9F5K9E552qJfiedpNYRERERkUhFG6wBahsiJy+rLvc+07fX6jX4A3Yq1et4Q8NbrNfKkB1NOgNMa0J6JkI3KHh7Vb9mPU0+x2XPvUbfTpVMeWsp23WuYuweO/HRBac0h+suXbpw+eWXc+mll/K9732Pnj170r5uA5VlZVSVl1FZVkrH8jLmf7uK3w8fwqfrNvLmd6t49P3/0eBz7FvTlUN6dOeIXj0TtEhEREREJL8UdbCOJjRsewnZyVavWzM0HBIHbGgZskOlK3CnwktITyV8Z3KG83y4rjsXoh13icGdh3+fr9fXsXrzFh5Z8hF9OlYwYoc+LB47moEzXmxet7S0lCVLlvDvU45iQ2MjdQ1NbGxsZGNjI2vWbOKUXn3oR3v6dW7P4Z1rcDvvwtebNrOgtpb7P/yEmnbtGFzdNYtHHF1jQxOb1m9Jejtfky8DrRERERGRfKZgHUcwZKczYLcmXENyATti2zjhNpehOyhe+M6XW4q1xbDtZcZuM2PYtltnA+9dVcHlr7zJkB41DOzWlYGHH8C4F+Y234rr/TNPoKZDe2po739fy9r5P216R07yZmb0raygb2UFlaWl3Pfhxzy47yAqy/TxJCIiIiKFQX+5elDb0OR5iLiX4eGtue46KBiwIbWQHbG/DF23mulh6fkQuMODaT4G7WTuHx1PsP9GduvGgCOGsnjVGhavXMOLX3zD+VVV7LPfflR/9j8WrVnLliYffSsr2L6ygoO7d2f/btW0L40850PPvWHtOvP30nJGz32D9iUl9GrXjl7t29O7XXv279KVfTt10aRnIiI5oFmhRUTiU7D2KB+r10GhITsoHWE7HZIJ7OmcgC0dgTudk6ZlOmynKzjHEq1/+lZV0reqkqO33waA1Ss28P669XzWpTM/2GE7OpWVsWzTZj5ev4FpX37Fje+9z36dunJwdQ17VHWkZ3m7iJBsZty0c3+cc6xpbOTbLfV8u6Wer+vr+e2Xn1NRUsKZffpyYJdqBWwRERERyRsK1hnktXqd6j2v48nnsB1LOoeqRwvcrQ3b+VTVznSQDvJyj+lgX1eWlbFvTTX71lQ3L+vVoQODq7tyXKfurG7Ywutrapm9cgWTv/iMRufYubKS7uXt6FxWRqfSMipKS2nw+djifJRbCQd3rWF4dTcAxvXqw2trVvPI11/xh6+/4tLtd8rMQYuIiIiIJEnBOknJDAuH9IRrSD1gh4oWtoMKNXQnE7hbG7YzGbTzUTKhOp7ge1dT3o7je/Ti+B7+a7VXNWzhk7o6Vjc2sL6xkfWNjazYUk+7khLKzahtbOCyj96je3k7RnXrwbbtO7Bg3VpWNWyhd7v21PkiZ/UXEREREckFBesU5Cpcw9aADa0L2eHihe50yUR4b23gDg+GqQbtdMxCni+8BGpILlRH0628Hd26tNu6bpRz8JwOPVi0eQP/WF3Ly41bGFrRmbt77kLv8naQ/ITdIiIiBUPXtYsUloII1mY2GtgDmOucW5jr9mRKoknNwoWGbEhv0M6EVMN7umZA9xK2Uw3awTBayAHba6CGxKHa67X1ic6JUjP2rejEvhX5NyGciIh4o4AoIsUg58HazHoBTzrnDg78PhUYADzvnLs5sNqewD3ABUDUYG1mVcCfgBrgC+CHzmXuozwTVesgr9XrcIUWtL1K1xD28LCXbND2ErILsYqdzkAN3kJ1NkZIiIiIiIhkS06DtZlVA48BVYHfxwClzrmhZvYHM9vVOfcR8CpwCfDHOLsbD8xzzt1uZg8DQ4A3M9n+TIdr8F69jiY8aIdq66HbS+BONminGrLzNWAnE6ghPaE6XYE63rmdLk2NPjan8DrOp9KMtF1tvfLY1o9PREQyJ9cV6yZgHPB04PcRwIzAz3OAg4CPnHMLgAUJ9rUMOMvMZjrnfpSBtqZFMuEa0hOwo8lGMIHcBfjwAJds0PYasgsxYCcbqCF3oTpb56mIiIiISGvkNFg759YBofejrcIfkAFWA4OT2NezZlYB/M3MXgUucc61mDbYzCYAEwC65PDQkw3XkLmAnWmZCEaphPXQUJfOkF1oATtToVokF1RdlLZO57iISOEoyXUDwmwAKgI/dySJ9pnZrsBsYBDQAzgzfB3n3BTn3BDn3JAqK6W6PPrDq2TWDbd6S2q3ClpR39T8KFbr6hpaPJK1aWVd88PT+rWbE1ZjN9du9hxA61ZtTCngtlYmX9PrZGXJULVaWsu5yIeIiIhIJuR6KHi4hfiHf88HBgJLk9j2R8B7zrnHzOwdwPt9k8KEB+bahsgQ25pQHZRK5TpUaLgutEp2OkULYF6r2slUsoPhsRAr2KmG6lxVqxWqRURERKSQ5FuwngW8ZmbbAKOBA5LY9j7gCTM7B1gLnJ6uRqUjRGdaeAW7mIM2pHa/b68h22vALqZbdYmIZJtGIIiISD7Ji2DtnBsR+O86MxsBjARud86tTWIfXwOHZqJ9mdTaqnUs0YaKF2vYTuU2ZMGQ3ZqAnUz1GjIXsHMx7Lw1VK0WERERkUKTF8E6lHOulq0zgxeF4PXWmQjYoeJdl53N0J2O68PTdRuyRCHba8BOV/Ua0huwWxOqczEMvBhDtZl1AaYBpcBG/HdK8AH/CzwAfuacezvG9lOBAcDzzrmbM99iEREREQmXd8E633ztq+e/TesYXdad0q2zl2dEpqrXXhTaZGjpqsh7DdmJAna6q9fQuoBdaFXqVBTaORvHGcDdzrmXzOxB4CjgK+AvzrmJ8TY0szFAqXNuqJn9wcx2dc59lIU2i0gSNGxdRKTtU7COY7Pz8deG5ZRgvNy4iiPLu8dcNzjBWWuvx85luC50rZ3MbV1dg6cKdmur15C5gF0MgTpbGht81K2vT3o7n8+1uLVfwBTn3JRo6zvnHgj5tQfwHf75JY41s0OBt4HznXONUTYfwdYRPnPwT/7YIlh7rWibWS/gSefcwXEOT0RERESiyLfbbeWVpb6NdLd2nNeuL+80beCg56dw3aaPI9aLNmt4a6R6Ky7ZKtVbknm5hVeiW3V5vT1XsoK36Qp/hC4rFvlerQ69tV/g0RyqzewhM/tnyOO6wPNDgWrn3HzgTeAI59x+QDlwdIyXqgKWBX5eDfQKXRha0Qb6BW5LGMHMqoHHAvsTERERkSQpWMexxfnoZGVUWil/nPUUF110Ec65qOE6KF0hW+E6PVK977fXgB13eRrvfR1PMQXqtsA5d75zbkTI40YzqwEmA+cGVlvinPsm8PMCIGogBjYAFYGfOxL5mT6CyIp2NE34r+1eF6/tZjbBzBaY2YIVK1bEW1WyINp9ujXkWEREJDeKNliXmn+ysPBHkM85PvLV0cXKuG7Txxx//PGUlJTw+uuvAzSH63RXq0Ot3tKkgJ1GqQbseNJVvc7V/aLjSWa4erzh7xKfmbUD/gpc5Zz7PPD042Y20MxKgROBxTE2X8jWsDwQODy0Gg78jDgV7SDn3Dovd2EIrcT36NHDw9G1nsKjiIiIFIKiDdaxVJRDk3O83rSGja6JFzZ8DYCZcfbZZ/Poo482rxurcp2JoeEK2OmTbMDORvUacjMLdzbFuza9yJ0HDAZ+FQjE44AbgceBRcA859zLZjbAzMKvkZ4FjDezu4GxwJjQajhwP/Er2tJG6QsJERGR7NLkZWEe3fQNnzZuptyM9778nPbt2zcvGzVqFI888kiL9e9p/JRLynbKSttCw7UmOGu9ZCc7SzS5WWtnDofUJjdrSzpXlhfdLbeccw8CD0ZZtHfYeu8B14Q9t87MRgAjgdujVJ2DFe35+CvaS9PTapHipC8nREQkFlUvwuxUWsEeZVUs/for+vbt22JZv379+PTTT3Fh/7Le0/hpxH4yOUQcVMVON68V7GxWr9t6BTtV+T5xWbY552qdczOcc8ujLJ5Fy4r28zEq3yIiIiLSCkVbse65z55ctGBBi+eampqYOmQIEydOpHfv3hHbdOzYkU6dOrF8+XL69OmTrabGFQzXqmC3XjCw5Uv1GnJfwe5Q3SGrAb/YqtWZFqOivZawynfI+iOy1jgRERGRNkQV64BNmzZx/vnnU11dzbhx42Kut/POO/PJJ5942memq9ahVMFOn2xXr9tKBTsbE5ipWp28BBVtEREREUmDoq1Yh1q6dCljx45ljz32YNasWZhZzHUPOeQQnnjiCQ46KNZda1qqbWiiujx71WRVsNMj2eo1ELOCnah6DclXsKH4rsPOdqhuamigfuOGpLfzNfky0BoRERERyWdFX7F+6aWXOOigg7jwwgv5y1/+QufOnWOuu2XLFq644gpmzJjBV1995fk1slm5DlIFOz2SnT08nkS35gLvFWwojCq2iIiIiEgxKOpgPWfOHM444wxmzpzJ+eefH7dS/dlnn9GrVy/effddGhoaqKqqymJLU6eA3XrJ3J7L6/BwBexI8fpNQ8BFREREJJ8V7VDwd999lx/84AfMmjXL07Duxx57jJqaGsaOHcsOO+xAdXV1Uq+X7SHh4Qp9iLiXLwcyfWzpnNwM0jtEHHI/0ZmIiIiISLEq2op1v379eP/99z1fK73PPvvQr18/hg4dyl133ZXSa+ZiSHi4Qqxge21vto4tndVrSDzBGXi7RVdQW6heh4rV34V2HoukwrnoDxEREckvRVuxrqiooEePHp7XP+SQQzjjjDNYtWoV7dq1a7HskrKdPO8nGK5zWb0GfygplOp1sJ35ULUOSufkZpCZ6nVbqFxrCLiIiIiIFIKiDdbJKisrw+eLnO03mVAdKtdDw6HwhofnYztzFbDb2tDwZO9frWq15IqqxSIiIhJN0Q4FT4bP5+Pqq69m6NChLarVqYbqoHwYGg6FOTw832R7grNsDQ3P5bByDQEXERERkUKhirUHb7/9Nk8//TSLFi1qfq61oTooX4aGQ+FVsPPRivomT9Vr8F7BjlW9Lsah4QrVIiIiIpKPFKw9WLFiBbvsskvzTODpCtWhFLDbjmSGh0PigJ1oeHhbHBoerVqd7VDta6hny/rVSW/nfI0ZaI0UOg0hFxERads0FNyDVatW0a1bNyAzoTpUbUNTXg0Rl9QFh4ena4h4toeGpzIM3EvAD5fs9dVB+fL/iYiIiIiIgrUHixYtYtddd83qawYDdq7DQ/D6a4Xs1kk2YMeSD9ddx5NMG5Kh80+ySbe3So36TUREipmGgifgnGP69On87W9/y1kbQsN1LoeKh4cbDRVPntdrsNfVNcQdGt7aYeFQ2Pe7zvUXTiIiIiIioVSxTmDhwoWUlZUxcODAXDcFaFnJznW4UDU7NV6r19moXOcj3btaRERERApNQVSszWw0sAcw1zm3MJuvPX36dMaOHYuZZfNlPcvHanahVbK9fDGQiWPyMslZvInN0lW5LjS5/kJJRCSdNGReRKRtyGnF2sy6mNmLZjbHzGaaWTszm2pm88zsmpBV9wTuBw6Ms6+qwD7mmtnjloYk7PP5mD59OuPGjWvtrrJClezkJNPG0GNK97G1pnrdlivXQfl+HomIiIiI5Hoo+BnA3c65UcBy4DSg1Dk3FOhnZsEZw14FLgFmxNnXeGCec244UA8MaW3jXnvtNaqrq9lrr71au6usy4eADfkbstPRnnQem5fh4cUcrkVERERE8llOh4I75x4I+bUHcCZwb+D3OcBBwEfOuQXAggS7WwacZWYznXM/Skf7/vSnP3HmmWemY1c5ky9DxSF/hotnIuSn69gSTW4Wa1KzQhgWHu8LAK+y+WVR45ZNbF67IuntfE26j7WIiIhIscmLa6zNbChQDXyGPyADrAYGe92Hc+5ZM6sA/mZmrwKXOOda/BVuZhOACQDbb7993P1t3ryZp556iiVLlnhtQt4LDyX5ck12uEyE7mxWzIOvlepxZCpcQ2r3mfZK1XERERERKVa5HgqOmdUAk4FzgQ1ARWBRR5JoX2DY+GxgEFur3y0456Y454Y454b06NEj7v5eeOEF9tlnH7bddluvTSg4+TTDeKho1zN7HXadyjaZ0prXzsSwcMhc+FWozl8x5q0IXydivotstlFERESk0OV68rJ2wF+Bq5xznwML8Q//BhiIv4Lt1Y+AkwJV6neAVpXmZsyYwWmnndaaXRSc8KCdT2E7VKqhO1dSbVuhhOt07y+8Wh9a+Q8dZXFP46dpfd22yMzGEH3einDh810cla02ioiIiLQFua5Yn4d/uPevzOyfgAHjzexuYCzwfBL7ug84O7Cf/YDHU23Upk2bmD17NieeeGKqu2gzCiVsF4J8C9fpCMS5qFRXl5dyT+OnPPPMM1l/7QI0gq2TPgbnrYjgnHvAOfdS4NcewHfR1jOzCWa2wMwWrFiR/PXnIiIiIm1VricvexB4MPQ5M3sGGAnc7pxbm8S+vgYOTUe75syZw+DBg0k0XDyWTU3pvSllRWl+3UM7WrjO9cRohSKV668TXXMdS7xrrpvXyZOJzZJxbd1H3HPPPdx11125bkreMbOHgP4hTw0HpgZ+TjhvRXC+C+fc/GjLnXNTgCkAQ4YM0d13RURERALyYvKyUM65WuLfVivjnnzySU455RTP66c7SKey/1yH73yafbwQrN7SlLZwHWsyM/AeroOSCdmZrFb3aF/aXK3f7HxUl5ewfEsjO9x/JYMHD8bn8/Hvf/+bHXfcMWNtKETOufNDfzez+/A4b0XIfBcnZ6yBEpXTVxQiIiIFL9dDwfNOfX09zz//PCeddJKn9TMdqr3a1OQiHrmioePeJHvtdbxh4bGGhENyt7nyOkQ8W0PAtzgft274jP80rGNq1zpeeOEFbrvtNt566y122GGHrLShwHmatyLKfBciecG56A8REZF8k3cV61x75ZVXGDBgAH369IlYdknZTs0/50ugjidWG7Nd3dbQ8fiSqV5nsnLdYv10T3CWwj2se7Qv5bG1K6l1jTzTbhOX/OQSbrjhhrS2qwjMAl4zs22A0cABZjYA+IFzLnSW8ND5Ln4FPOicm5711oqIiIgUKAXrMDNnzmTMmDERzwdDdSEE6kTiHUO2QrfCdkvJXHudrXCdLZ0ry6NW3Df4mvhXwxoOat+Z5T17cuWVV2a1Xb6GLdSvr016O9eUP6M0nHPrzGwELeetWAtcE7ZexHwXIiIiIuKdhoKHcM7x97//naOPPrrF820pVCcSbUh5toaWR5uBvNiGknsdGt6aYeGpVI8zrXNlecQXAqVAo3OMrerB888/T4cOhTXJWr5wztU652Y455bnui0iIiIibZUq1iE++OADzIz+/bdOqltMoTqRXA0tjxeu22KV2+vQ8FQr11AY1euKklI6l5TR6By77bZbjlsmIiIiIhKbgnWIOXPmMGrUKMz8QVGh2pto/ZPLIeWJFEIYT3bW8GjyJVwnWyEPtnnlxnrWuya6luhjSkRERETym/5iDfH3v/+dc889F1Cobq1chu1EWju8PFvB3Eu4TnSPay/hGsjL6vX/+TawU1kHKkvy/4sQERERESluusY6YM2aNbzxxhscfvjhCtUZki+3A2utbF4L7uWa63jXW0P8a66D8u3a6wbn4w+133Bxj23jfjEgkk90aygREZHipWAd8Oc//5kjjzySG3sMLvjgVyjaStAOylTQzla4htRui5UJXzTU07OsnO3Ki2PCMjOrMbORZtY9120RERERkeQpWAc8/PDDbJzxzzYR8ApVWw3a6ZDtcJ3rgL3R18QW59js8+W0HdlgZtXAc8B+wKtm1iPw/FQzm2dm1yTY3tN6Irmkar6IiLR1CtbAm2++yUdvLaEvxVEdKxS5vPVXOqUrYKcrXCcbsFsbslO5fnuv9lXs1q6Cv61b0arXLhB7A5c6524B/g4MNrMxQKlzbijQz8x2jbah1/VEREREJLOKNlg3NDTwm9/8hoMPPphh++3PPnTByI/JtSSxeKE7XwN5OoaJpyNcg/fqdVC6QrZXZsaxnbrzj421NLicVK2/8tWtpGnLpqQejZvW4XxNmNkEM1sQ8pgQ64Wcc3Odc/PN7BD8Vet5wAhgRmCVOcBBMTb3up6IiIiIZFDRzgr+7rvv8vnnn9Pp9Q/5IdtSVrzfMRSVXN2LO1xtQ1PKs4unY7ZwSDxjeCxewnWyVepo6w+hkp02r2ZmwxqOT2pvreec+7ak6464ulVYZTfv2635HyXVO9H09aopwJRo65jZQ0D/kKf+AdwEjANqgQagClgWWL4aGBzjJb2uJyIiIiIZVLTBes8998T9/kW2pyLXTZE8kIvbgwUr15m6fZfXcA2kfebt0PBd0b2Siu6VEc/Fs76xkUmffczImh7c+8WnXLl0Kf3794+7Tbq5tZ+P8pW1n1NaOczb+s7hW/URrm5F7wTrnR9j0U/N7CbgeGADNH84dST26CKv64mIiIhIBhXtH2HLFr+f6yZInsvWUPJUhoZ7GRIO3oaFQ/JDw5MRHEIeDNheqtntSkp4f+MGJn32MQ7HX/7yl4y1L45XfHUrcU1bPK3s6lZCu444575N5kXMbKKZ/TDwa1dgDbCQrcO6BwKfxdjc63oiIiIikkFFW7EWSUV4uE5XVTs0XHutYAfDdTqGhUPmqtepmLLsC9Y0NnJETXcWrV9HXV32Zyl3zvlK+34fX+0nlHbfI+H6vlVLcWs+HZHCS00BZpjZj4B38F8r3Ql4zcy2AUYDB5jZAOAHzrnQ2b9nha+XwuuLiIiISCsVbcVaJB0yUdFOtoLtdUKzZKrXmaxge3FEjf92zi+vXskdu+7O+PHjc9IO39cLerjVH+MS3BfINdbjNq0G+Feyr+Gcq3XOjXTOHeKcu9D5rcM/Mdl84FDn3Frn3HthoZpo6yX7+lKcdOsrERGR9FKwFkmjdIXsZGcPT/fQcEh/wE5mRvHdK6vYv3NXvlfVkR0rKtlrr73S1o5kOOdW0r4Lru67uOv5aj+mpGZnXKIEntxr1zrnZjjnlqdjPZFiovtmi4hItilYi2RIOkJ2MgF79ZamtFevYWvAzmYV28yYuOPOLN9Sz3/X5bYI62o/Oci3cmns5c7hW/0JvmX/qclis6SVFLpEREQknRSsRbKgtSE719XroGwG7Orycq7YYWfu+vx/WXm9ON6gfi2ucXPUhW7jt1hFDc652iy3S0RERETyhIK1SJalGrCTrV57kWz1OihbAfv7nbuwprGR2trcZVbnnLOaXfCt/jjqct/KD3CrP9o/y80SERERkTyiYC2SI6lWsdM9NBzyN2CbGTt2rOT993N7ezzfV/O7+Go/iZjEzDVsgi0bAN7MScNE2gBdDy0iIm2BgrVIHkg2ZGfi2mtoXcD2IpkJzIK+X1PNxIkTc121XmeV3XEbvmnxvG/1R5R02zWtk5blipnVmNlIM+ue67aIiIiIFBoFa5E8k08BOxOziCcbrn9Y04d9992XE044Iant0s2t+nCwb+UHW393Dl/tpzR9+UanHDYrITObambzzOyaOOtUA88B+wGvmlmPrDVQREREpA1QsBbJU/kQsCG1WcTTqcSMm266iYULF7J+/fq07jsZzrm3aNyEa/B/MeDWL6OkY0+ccxty1qgEzGwMUOqcGwr0M7NdY6y6N3Cpc+4W4O/A4Gy1UURERKQtyHmw9jL80MxGm9mlZrZvNtsmkg8KMWAnCtfJVq07derEKaecwq9//euktku3kprd8K36EPBPWuZb+cHeOW1QYiOAGYGf5wAHRVvJOTfXOTffzA7BX7WeF209M5tgZgvMbMGKFSsy0V4RERGRgpTTYB1t+GGMYYt7AvcDB8bZV5WZzTSzuWb2uJlZZlsvkl2FFrDTHa7vuOMO/vjHP+Z0IrOmL1+v8q35DFe/Hpq24Jx7O2eNicLMHjKzfwYfwM+AZYHFq4FecbY1YBxQC0R985xzU5xzQ5xzQ3r00GhxERERkaBcV6zDhx8eRvRhi68Cl7C18hLNeGCec244UA8MyVyzRXIn0wE7nROdpXNYeM+ePfn+97/Pxx9Hv+1VNjjn6ko69qHp87mUdOufs3bE4pw73zk3IvjA/4VkRWBxR+J85ju/nwJLgOMz3lgRERGRNiSnwTrK8MMjiTJs0Tm3wDl3h3Pu2zi7WwacZGa7Oud+5JzT7W+kTUvmNl1ew3VQsgE7VclWrVevXk1NTU3Kr5cOvpXvDXANdTR98a+KxGvn3EK2Dv8eCHwWbSUzm2hmPwz82hVYk+mGiYiIiLQlluu7xASGH/4W2BZYCdzvnFtsZqOAwc653ySxr7HAtQQq3M65prDlE4AJgV+7B14vUzK9/2R0AdbmuhEB+dIWvT+R8qUd4P39WemcOyrTjSlUZtYZeA14BRgNHAD0BX7gnLsmZL1q/F9qtgfeAX6a6BZiZrYC+DxDTU+3fPr/Pd+ob2JT38SmvolNfROb+iY29U1s+dY3Ozjnol4Pl/NgHWRmNwGnAOcEqthjgN2dc7d63H5X4FtgI/AnYLZz7rGMNThxexY45/JiOLqZTXHOTUi8ZublS1v0/uRvOyC/3p9CFwjNI4F/OeeW57o9uaDzKTb1TWzqm9jUN7Gpb2JT38SmvomtkPom15OXhQ8//A0ehi3G8CPgpECV+h2gQ5qa2RY8m+sGhMintuSLfOmTfGmHpJFzrtY5N6NYQ7WIiIhINpTl+PWnADPM7Ef4w/As4F9mtg1bhy16dR/whJmdg3846+lpbmvBcs7lTWDKp7bki3zpk3xph4iIiIhIoclpsHbO1eIfotjMzEYEnrvdOef5ek/n3NfAoelsXytNyXUDJC69P/lN74+kk86n2NQ3salvYlPfxKa+iU19E5v6JraC6Zu8ucZaREREREREpBDl+j7WIiIiIiIiIgVNwboVzKzGzEaaWfeQ50ab2aVmtm8u2yZ6fwqJ3isRERERKWQK1h6ZWZmZfWFm/ww8DgaeA/YDXjWz4P3M9gTuBw6Ms68bzOz9kH0NyvgBtHFmVm1mL5jZAjN7KHCLIb0/ecDMepnZa3F+13slrRJ6TpnZTmb2vJm9ZmZ3BZ4L//zeK/D8VDObZ2bXxNt/IUvUNyHrPWBmx4X8XvR9Y2YXhJwzi8zsocDz6puwf3NDtlPfxPj/rK33jZl1MbMXzWyOmc00s3bRjtnrc21FEv3S4m+jwHNttl/AW99EWyfwfF72jYK1d3sDf3HOjXDOjcDfd5c6524B/g4MDqz3KnAJMCPB/m4J7ss5tyhDbS4m44EnAve564T//dL7k2OB0PwYUBXt9wC9V5KyKOfUbcBNzrmDgW3NPyFmi89v59zbZjYGKHXODQX6mdmuOWh+RnnsG8z/RXHv4J0B1Df+vnHOPRjyb/5rwP9T3zSfNy3+zTWzIeqb5r6JeK4Y+gY4A7jbOTcKWA6cRtgxR+uHIugbL/0S8bdREfQLeOibKOsclc99o2Dt3QHAsWb2HzObCvzbOTffzA7BX2mbB+CcW+Ccu8M5920uG1uEVgF7mllXYDvgA70/eaEJGAesi/E7zrm5eq+kFcLPqd2A/wZ+/g7oQtjnt5mVASPY+qXNHOCgrLU4exL2jZmVA/8P+MzMTggsG4H6pktwRTPrC/Ryzi1AfRPsm/B/c79EfRPsm2jPjaCN941z7gHn3EuBX3sAZxJ5zCM8PtdmeOyXiL+NaOP9At76Jso635HHfaNg7d2bwBHOuf2AcuBoMzP8/yPUAg1J7u9XtnWIWWma21qMXgd2AH4OvA+s1vuTe865daG3zQv/PUjvlaQqyjn1JHC9+Yc1HwW8QpTPb/yVgWWBbVYDvbLX6uzw2Dc/BN4Dbgf2M7Ofob4J9k3QT4EHAz+rb/x9E/FvLuqbYN9Ee67N902QmQ0FqvF/2RJ+zNH6oSj6Jl6/xPjbqCj6BRKeMy3Wcc7NJ4/7RsHauyXOuW8CPy8AdnV+PwWWAMcnub/Q4atNaW1pcboe+Ilz7kbgA+AcvT+FQ++VpItz7mbgReBHwGPOuQ1E+fwGNgAVgec6UgT/Hsbom32AKc655cCfgENR3wT7BjMrwd8n/wysqr7x903Ev7mobx5zzm2I0V9F0TdmVgNMBs4l+jF7fa5N8dAv0bT5fgFvfRO2DrHWywd505AC8LiZDQxUxE4Eyszsh4FlXYE1OWqX+FUDewXen/2Bar0/hcHMJuq9kjRbBGwP3B34PfzzezGwkK3DxwYCn2W3iTmziJZ98zHQL/DzEOBz1Dd3hzx3MPB/zjkX+F194xf+b65DfXN3nOfafN8EJpX6K3CVcy7W54jX59oMj/0STZvuF/DWN1HWIdp6WWt0AmW5bkABuRH4M2DAM8AUYIaZ/Qh4B/8Y/2T8KrAtwIPOuelpa2lxmgQ8gn9o2jz879Uf9P4UBP2/JOl2Of7JTuoCv7f4/HbOvWxmnYHXzGwbYDT+67CLQXjfTMX/WXka/mHypwDrUd8EHQn8K+T3WahvIPLf3L/gL9aob6I/N4u23zfn4Z989Fdm9iv858f4sGN2RPZDtOfaEi/9Es0s2na/gLe+CV/nQfK4b2zrl7AiIiLFIzAT60jgX4Gh0BKgvolNfROb+ia2YuybaMfs9bm2zOvxFlu/QOH3jYK1iIiIiIiISCvoGmsRERERERGRVlCwFhEREREREWkFBWsRERERERGRVlCwFhEREREREWkFBWsRERERERGRVvj/KWHnXfsX1G8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1440x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 绘图部分\n",
    "fig =plt.figure(figsize=(20,6))\n",
    "# SVD空间场\n",
    "ax1=fig.add_axes([0.1,0.55,0.48,0.4],projection=ccrs.PlateCarree(central_longitude=105))\n",
    "ax3=fig.add_axes([0.1,0.05,0.5,0.4],projection=ccrs.PlateCarree(central_longitude=83))\n",
    "createmap(ax1,70,125,15,55)\n",
    "createmap(ax3,46,110,-30,20)\n",
    "# 设置标题\n",
    "ax1.set_title('(a) 我国气温svd(1948-2021)',loc='left')\n",
    "ax1.set_title(str(round(frac[0].data*100,2))+'%',loc='right')\n",
    "ax3.set_title('(c) 印度洋海温svd(1948-2021)',loc='left')\n",
    "ax3.set_title(str(round(frac[0].data*100,2))+'%',loc='right')\n",
    "# chinasvd\n",
    "ax1.contourf(lon,lat,lp[0,:,:],cmap=plt.cm.RdBu_r,zorder=0,extend='both',levels=np.arange(-30,11,1),transform=ccrs.PlateCarree())\n",
    "# ZDsvd\n",
    "colorbar=ax3.contourf(lon1,lat1,rp[0,:,:],cmap=plt.cm.RdBu_r,zorder=0,extend='both',levels=np.arange(-30,11,1),transform=ccrs.PlateCarree())\n",
    "colorbarax=fig.add_axes([0.45,0.05,0.01,0.9])\n",
    "plt.colorbar(colorbar,cax=colorbarax,format='%.1f',shrink=0.5)\n",
    "#SVD时间序列\n",
    "ax2=fig.add_axes([0.5,0.55,0.4,0.4])\n",
    "ax4=fig.add_axes([0.5,0.05,0.4,0.4])\n",
    "ax2.set_title('(b) 我国气温序列',loc='left')\n",
    "ax4.set_title('(d) 印度洋海温时间序列',loc='left')\n",
    "###\n",
    "le1,year=smoothsvd(leYD)\n",
    "bar=ax2.bar(year,le1,color='black')\n",
    "for i in bar:\n",
    "    if i.get_height()>0:\n",
    "        i.set_facecolor('red')\n",
    "    else:\n",
    "        i.set_facecolor('blue')\n",
    "ax2.axhline(y=0,linestyle='--',color='black')\n",
    "#-----------------------------------------------------------------------------------#\n",
    "re1,year=smoothsvd(reYD)\n",
    "bar=ax4.bar(year,re1,color='black')\n",
    "for i in bar:\n",
    "    if i.get_height()>0:\n",
    "        i.set_facecolor('red')\n",
    "    else:\n",
    "        i.set_facecolor('blue')\n",
    "ax4.axhline(y=0,linestyle='--',color='black')\n",
    "##保存图片\n",
    "plt.savefig('../../picture/Pythonhome/12/ex6.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    " # 第六问"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 对于y= aX1+bX2来说\n",
    "# 中东太平洋海温和印度洋海温的第一模态的时间系数当作X1，Y1\n",
    "# 则中国夏季海温为Y 求解出回归系数 a，b   多元回归\n",
    "#-----------------选出中国夏季气温和时间系数夏季的部分------------------#\n",
    "airchinasum=airchina.loc[airchina.time.dt.month.isin([6,7,8])]\n",
    "resum=re[0,:].loc[re.time.dt.month.isin([6,7,8])]\n",
    "reYDsum=reYD[0,:].loc[reYD.time.dt.month.isin([6,7,8])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X=np.zeros((len(resum),2))\n",
    "X[:,0]=resum.data\n",
    "X[:,1]=reYDsum.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "Y=airchinasum.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "b=np.zeros((2,17,27))\n",
    "for i in range(17):\n",
    "    for j in range(27):\n",
    "        temp=np.dot(np.linalg.inv(X.T.dot(X)),X.T)\n",
    "        b[:,i,j]=np.dot(temp,Y[:,i,j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;rightExpansionCoefs&#x27; (time: 222)&gt;\n",
       "array([-0.30619344, -1.0457968 , -1.2728809 , -0.30895728, -0.9374897 ,\n",
       "       -1.2658421 , -0.30088934, -0.82086474, -1.1107835 , -0.32044253,\n",
       "       -1.0049199 , -1.3692237 , -0.28450206, -0.8567748 , -1.2117362 ,\n",
       "       -0.4282964 , -1.085014  , -1.4068748 , -0.4844414 , -1.0369544 ,\n",
       "       -1.3070364 , -0.3068242 , -0.8792356 , -1.1557281 , -0.26479954,\n",
       "       -0.85061115, -1.2566025 , -0.49838805, -1.0579575 , -1.3763396 ,\n",
       "       -0.5831986 , -1.0656952 , -1.5107518 , -0.5632471 , -1.117295  ,\n",
       "       -1.4588815 , -0.4673167 , -1.038538  , -1.3912618 , -0.31961957,\n",
       "       -0.9717505 , -1.3293898 , -0.42012364, -1.0385233 , -1.4428761 ,\n",
       "       -0.54917103, -1.0030856 , -1.3594949 , -0.35265902, -0.9044527 ,\n",
       "       -1.2026204 , -0.38036934, -0.9105289 , -1.3205816 , -0.53545195,\n",
       "       -1.1142288 , -1.4161389 , -0.41959217, -0.99743927, -1.4678909 ,\n",
       "       -0.54614   , -1.0904751 , -1.4128214 , -0.3791778 , -0.98646873,\n",
       "       -1.3217164 , -0.37971106, -0.9217302 , -1.2877914 , -0.25125983,\n",
       "       -0.8366899 , -1.2072453 , -0.22896162, -0.9115278 , -1.3346969 ,\n",
       "       -0.27935442, -0.8319695 , -1.1561897 , -0.27162844, -0.80447555,\n",
       "       -1.199861  , -0.25100744, -0.81357   , -1.1103876 , -0.3286106 ,\n",
       "       -0.9140418 , -1.3378764 , -0.46965116, -1.0730408 , -1.4810629 ,\n",
       "       -0.37091458, -0.95450914, -1.3665158 , -0.26623303, -0.87762225,\n",
       "       -1.3262032 , -0.34456792, -0.90577936, -1.2162867 , -0.34775892,\n",
       "...\n",
       "       -1.1840292 , -0.46211767, -0.9699686 , -1.370758  , -0.54741436,\n",
       "       -1.046852  , -1.362644  , -0.56588644, -1.0819968 , -1.3094829 ,\n",
       "       -0.5909658 , -1.0761638 , -1.4093655 , -0.55777425, -1.115772  ,\n",
       "       -1.4036974 , -0.40379173, -0.91468525, -1.1870216 , -0.43010712,\n",
       "       -0.9847185 , -1.3234999 , -0.48681983, -1.1313038 , -1.5372065 ,\n",
       "       -0.3972044 , -0.925964  , -1.2822559 , -0.11727703, -0.65372455,\n",
       "       -1.0280427 , -0.31659642, -0.8690922 , -1.2683027 , -0.32219607,\n",
       "       -0.8701623 , -1.1770196 , -0.27564716, -0.8856361 , -1.2809787 ,\n",
       "       -0.38210875, -0.9072541 , -1.3044515 , -0.46822652, -1.0487064 ,\n",
       "       -1.4099733 , -0.47667623, -0.9652399 , -1.335974  , -0.35822785,\n",
       "       -1.0316038 , -1.3401202 , -0.31729397, -0.8742235 , -1.2594297 ,\n",
       "       -0.23217277, -0.79293936, -1.1293558 , -0.3854139 , -0.9937175 ,\n",
       "       -1.3557746 , -0.3090404 , -0.81169236, -1.1098711 , -0.30653498,\n",
       "       -0.7983621 , -1.1334221 , -0.2594755 , -0.8400097 , -1.2416923 ,\n",
       "       -0.41528386, -0.97311056, -1.2958498 , -0.52326614, -1.1086522 ,\n",
       "       -1.5276273 , -0.6369859 , -1.2797099 , -1.7145532 , -0.55626154,\n",
       "       -1.080611  , -1.3190219 , -0.39797458, -1.0872021 , -1.4239187 ,\n",
       "       -0.40424576, -1.0033784 , -1.3506713 , -0.5224437 , -1.1770903 ,\n",
       "       -1.4983455 , -0.5175694 , -1.0855566 , -1.3708183 , -0.37166142,\n",
       "       -0.8611758 , -1.2353214 ], dtype=float32)\n",
       "Coordinates:\n",
       "    n        int32 0\n",
       "  * time     (time) datetime64[ns] 1948-06-01 1948-07-01 ... 2021-08-01\n",
       "Attributes:\n",
       "    long_name:  Time expansion coefficients for sst.</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'rightExpansionCoefs'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 222</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c4d894c2-30cf-4c98-ac0b-8766ffed73bb' class='xr-array-in' type='checkbox' checked><label for='section-c4d894c2-30cf-4c98-ac0b-8766ffed73bb' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.3062 -1.046 -1.273 -0.309 -0.9375 ... -1.371 -0.3717 -0.8612 -1.235</span></div><div class='xr-array-data'><pre>array([-0.30619344, -1.0457968 , -1.2728809 , -0.30895728, -0.9374897 ,\n",
       "       -1.2658421 , -0.30088934, -0.82086474, -1.1107835 , -0.32044253,\n",
       "       -1.0049199 , -1.3692237 , -0.28450206, -0.8567748 , -1.2117362 ,\n",
       "       -0.4282964 , -1.085014  , -1.4068748 , -0.4844414 , -1.0369544 ,\n",
       "       -1.3070364 , -0.3068242 , -0.8792356 , -1.1557281 , -0.26479954,\n",
       "       -0.85061115, -1.2566025 , -0.49838805, -1.0579575 , -1.3763396 ,\n",
       "       -0.5831986 , -1.0656952 , -1.5107518 , -0.5632471 , -1.117295  ,\n",
       "       -1.4588815 , -0.4673167 , -1.038538  , -1.3912618 , -0.31961957,\n",
       "       -0.9717505 , -1.3293898 , -0.42012364, -1.0385233 , -1.4428761 ,\n",
       "       -0.54917103, -1.0030856 , -1.3594949 , -0.35265902, -0.9044527 ,\n",
       "       -1.2026204 , -0.38036934, -0.9105289 , -1.3205816 , -0.53545195,\n",
       "       -1.1142288 , -1.4161389 , -0.41959217, -0.99743927, -1.4678909 ,\n",
       "       -0.54614   , -1.0904751 , -1.4128214 , -0.3791778 , -0.98646873,\n",
       "       -1.3217164 , -0.37971106, -0.9217302 , -1.2877914 , -0.25125983,\n",
       "       -0.8366899 , -1.2072453 , -0.22896162, -0.9115278 , -1.3346969 ,\n",
       "       -0.27935442, -0.8319695 , -1.1561897 , -0.27162844, -0.80447555,\n",
       "       -1.199861  , -0.25100744, -0.81357   , -1.1103876 , -0.3286106 ,\n",
       "       -0.9140418 , -1.3378764 , -0.46965116, -1.0730408 , -1.4810629 ,\n",
       "       -0.37091458, -0.95450914, -1.3665158 , -0.26623303, -0.87762225,\n",
       "       -1.3262032 , -0.34456792, -0.90577936, -1.2162867 , -0.34775892,\n",
       "...\n",
       "       -1.1840292 , -0.46211767, -0.9699686 , -1.370758  , -0.54741436,\n",
       "       -1.046852  , -1.362644  , -0.56588644, -1.0819968 , -1.3094829 ,\n",
       "       -0.5909658 , -1.0761638 , -1.4093655 , -0.55777425, -1.115772  ,\n",
       "       -1.4036974 , -0.40379173, -0.91468525, -1.1870216 , -0.43010712,\n",
       "       -0.9847185 , -1.3234999 , -0.48681983, -1.1313038 , -1.5372065 ,\n",
       "       -0.3972044 , -0.925964  , -1.2822559 , -0.11727703, -0.65372455,\n",
       "       -1.0280427 , -0.31659642, -0.8690922 , -1.2683027 , -0.32219607,\n",
       "       -0.8701623 , -1.1770196 , -0.27564716, -0.8856361 , -1.2809787 ,\n",
       "       -0.38210875, -0.9072541 , -1.3044515 , -0.46822652, -1.0487064 ,\n",
       "       -1.4099733 , -0.47667623, -0.9652399 , -1.335974  , -0.35822785,\n",
       "       -1.0316038 , -1.3401202 , -0.31729397, -0.8742235 , -1.2594297 ,\n",
       "       -0.23217277, -0.79293936, -1.1293558 , -0.3854139 , -0.9937175 ,\n",
       "       -1.3557746 , -0.3090404 , -0.81169236, -1.1098711 , -0.30653498,\n",
       "       -0.7983621 , -1.1334221 , -0.2594755 , -0.8400097 , -1.2416923 ,\n",
       "       -0.41528386, -0.97311056, -1.2958498 , -0.52326614, -1.1086522 ,\n",
       "       -1.5276273 , -0.6369859 , -1.2797099 , -1.7145532 , -0.55626154,\n",
       "       -1.080611  , -1.3190219 , -0.39797458, -1.0872021 , -1.4239187 ,\n",
       "       -0.40424576, -1.0033784 , -1.3506713 , -0.5224437 , -1.1770903 ,\n",
       "       -1.4983455 , -0.5175694 , -1.0855566 , -1.3708183 , -0.37166142,\n",
       "       -0.8611758 , -1.2353214 ], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-a3d3e96d-a338-47e3-9ba5-3e1c72bee61f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a3d3e96d-a338-47e3-9ba5-3e1c72bee61f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>n</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-ab8c890d-558c-4020-a224-5294d7f41324' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ab8c890d-558c-4020-a224-5294d7f41324' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2415770-c5d4-4f83-9f0a-87c3e0b9e416' class='xr-var-data-in' type='checkbox'><label for='data-b2415770-c5d4-4f83-9f0a-87c3e0b9e416' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1948-06-01 ... 2021-08-01</div><input id='attrs-9c85a256-8467-499a-a9c4-a32faa5b998e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c85a256-8467-499a-a9c4-a32faa5b998e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47485430-c715-4abe-b2d0-ea4cc25fe9b1' class='xr-var-data-in' type='checkbox'><label for='data-47485430-c715-4abe-b2d0-ea4cc25fe9b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 81053.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1948-06-01T00:00:00.000000000&#x27;, &#x27;1948-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1948-08-01T00:00:00.000000000&#x27;, ..., &#x27;2021-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-07-01T00:00:00.000000000&#x27;, &#x27;2021-08-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7d5d3c86-a9d3-43ec-b51a-25d729229d2c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7d5d3c86-a9d3-43ec-b51a-25d729229d2c' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time expansion coefficients for sst.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'rightExpansionCoefs' (time: 222)>\n",
       "array([-0.30619344, -1.0457968 , -1.2728809 , -0.30895728, -0.9374897 ,\n",
       "       -1.2658421 , -0.30088934, -0.82086474, -1.1107835 , -0.32044253,\n",
       "       -1.0049199 , -1.3692237 , -0.28450206, -0.8567748 , -1.2117362 ,\n",
       "       -0.4282964 , -1.085014  , -1.4068748 , -0.4844414 , -1.0369544 ,\n",
       "       -1.3070364 , -0.3068242 , -0.8792356 , -1.1557281 , -0.26479954,\n",
       "       -0.85061115, -1.2566025 , -0.49838805, -1.0579575 , -1.3763396 ,\n",
       "       -0.5831986 , -1.0656952 , -1.5107518 , -0.5632471 , -1.117295  ,\n",
       "       -1.4588815 , -0.4673167 , -1.038538  , -1.3912618 , -0.31961957,\n",
       "       -0.9717505 , -1.3293898 , -0.42012364, -1.0385233 , -1.4428761 ,\n",
       "       -0.54917103, -1.0030856 , -1.3594949 , -0.35265902, -0.9044527 ,\n",
       "       -1.2026204 , -0.38036934, -0.9105289 , -1.3205816 , -0.53545195,\n",
       "       -1.1142288 , -1.4161389 , -0.41959217, -0.99743927, -1.4678909 ,\n",
       "       -0.54614   , -1.0904751 , -1.4128214 , -0.3791778 , -0.98646873,\n",
       "       -1.3217164 , -0.37971106, -0.9217302 , -1.2877914 , -0.25125983,\n",
       "       -0.8366899 , -1.2072453 , -0.22896162, -0.9115278 , -1.3346969 ,\n",
       "       -0.27935442, -0.8319695 , -1.1561897 , -0.27162844, -0.80447555,\n",
       "       -1.199861  , -0.25100744, -0.81357   , -1.1103876 , -0.3286106 ,\n",
       "       -0.9140418 , -1.3378764 , -0.46965116, -1.0730408 , -1.4810629 ,\n",
       "       -0.37091458, -0.95450914, -1.3665158 , -0.26623303, -0.87762225,\n",
       "       -1.3262032 , -0.34456792, -0.90577936, -1.2162867 , -0.34775892,\n",
       "...\n",
       "       -1.1840292 , -0.46211767, -0.9699686 , -1.370758  , -0.54741436,\n",
       "       -1.046852  , -1.362644  , -0.56588644, -1.0819968 , -1.3094829 ,\n",
       "       -0.5909658 , -1.0761638 , -1.4093655 , -0.55777425, -1.115772  ,\n",
       "       -1.4036974 , -0.40379173, -0.91468525, -1.1870216 , -0.43010712,\n",
       "       -0.9847185 , -1.3234999 , -0.48681983, -1.1313038 , -1.5372065 ,\n",
       "       -0.3972044 , -0.925964  , -1.2822559 , -0.11727703, -0.65372455,\n",
       "       -1.0280427 , -0.31659642, -0.8690922 , -1.2683027 , -0.32219607,\n",
       "       -0.8701623 , -1.1770196 , -0.27564716, -0.8856361 , -1.2809787 ,\n",
       "       -0.38210875, -0.9072541 , -1.3044515 , -0.46822652, -1.0487064 ,\n",
       "       -1.4099733 , -0.47667623, -0.9652399 , -1.335974  , -0.35822785,\n",
       "       -1.0316038 , -1.3401202 , -0.31729397, -0.8742235 , -1.2594297 ,\n",
       "       -0.23217277, -0.79293936, -1.1293558 , -0.3854139 , -0.9937175 ,\n",
       "       -1.3557746 , -0.3090404 , -0.81169236, -1.1098711 , -0.30653498,\n",
       "       -0.7983621 , -1.1334221 , -0.2594755 , -0.8400097 , -1.2416923 ,\n",
       "       -0.41528386, -0.97311056, -1.2958498 , -0.52326614, -1.1086522 ,\n",
       "       -1.5276273 , -0.6369859 , -1.2797099 , -1.7145532 , -0.55626154,\n",
       "       -1.080611  , -1.3190219 , -0.39797458, -1.0872021 , -1.4239187 ,\n",
       "       -0.40424576, -1.0033784 , -1.3506713 , -0.5224437 , -1.1770903 ,\n",
       "       -1.4983455 , -0.5175694 , -1.0855566 , -1.3708183 , -0.37166142,\n",
       "       -0.8611758 , -1.2353214 ], dtype=float32)\n",
       "Coordinates:\n",
       "    n        int32 0\n",
       "  * time     (time) datetime64[ns] 1948-06-01 1948-07-01 ... 2021-08-01\n",
       "Attributes:\n",
       "    long_name:  Time expansion coefficients for sst."
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# 回归预测2021年夏季气温\n",
    "re2021=resum.loc['2021'].data\n",
    "reYD2021=reYDsum.loc['2021'].data\n",
    "air2021=np.zeros((3,17,27))\n",
    "for i in range(3):\n",
    "    air2021[i,:,:]=b[0,:,:]*re2021[i]+b[1,:,:]* reYD2021[i]\n",
    "air2021=air2021.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(16,9))\n",
    "ax=fig.subplots(1,1,subplot_kw={'projection':ccrs.PlateCarree(central_longitude=105)})\n",
    "createmap(ax,70,125,15,55)\n",
    "ax.set_title('svd回归2021年的中国夏季气温')\n",
    "colorbar=ax.contourf(lon,lat,air2021[:,:],cmap='bwr',zorder=0,extend='both',transform=ccrs.PlateCarree())\n",
    "plt.colorbar(colorbar, extendrect='True', pad=0.03, fraction=0.04, shrink=0.7)\n",
    "\n",
    "# 保存图片\n",
    "plt.savefig('../../picture/Pythonhome/12/ex7.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
